MDGF 1656 · Indicators for the proposed M&E framework 147 Reconfiguring the existing reporting...
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MDGF 1656 Strengthening the Philippines Institutional Capacity to Adapt to Climate Change Health Sector Book I Final Report April 27, 2011 Institute of Health Policy and Development Studies National Institutes of Health UP Manila
Transcript of MDGF 1656 · Indicators for the proposed M&E framework 147 Reconfiguring the existing reporting...
MDGF 1656
Strengthening the Philippines Institutional Capacity to Adapt
to Climate Change
Health Sector
Book I
Final Report
April 27, 2011
Institute of Health Policy and
Development Studies
National Institutes of Health UP Manila
MDGF 1656: Conduct of Climate Change Vulnerability
and Impact Assessment Framework,
Development of a Monitoring and Evaluation
Framework/System, and Compendium of Good and
Innovative Climate Change Adaptation Practices
(Health Sector)
Book I
FINAL REPORT
April 27, 2011
Institute of Health Policy and Development Studies
National Institutes of Health
University of the Philippines Manila
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Project Team
Fely Marilyn E. Lorenzo, RN, MPH, DrPH
Principal Investigator
Tonie O. Balangue, PhD,
Jennifer Frances E. dela Rosa, RN, MPH, MSc HPPF
Jonathan David C. Flavier, MD, MA
Fernando B. Garcia, MPA
Aguedo Troy D. Gepte IV, MD, MPH
Jesus V. Lorenzo, MEEc
Victorio B. Molina, MPH
Elvira E. Dumayas, MS
Ramon A. Razal, PhD,
Ma. Esmeralda C. Silva, MPPM
Co – investigators
Kim T. Estella, RN
Joanna Grace R. Fernandez, BSPH
Oliver Patrick O. Montevirgen, RN
Maria Karen Patricia A. Quimson, BSES
Carlo Carlos, BSES
Pura Beatriz S.Valle, BSF
Research Assistants
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Table of Contents
Table of Contents IV
List of Tables and Figures VI
Acronyms and Abbreviations Used VIII
Executive Summary X
I. INTRODUCTION 1
Health Millennium Development Goals 3
Project Objectives 4
II. CONCEPTUAL AND PROCESS FRAMEWORK OF THE HEALTH SECTOR
STUDY 6
A. Concepts and Terminologies 6
B. Review of Literature 8
The Challenge of Climate Change 8
The Philippines and Climate Change 9
Climate Change Vulnerability and Assessment 10
Effects of Climate Change on Health 11
Monitoring and Evaluation of Climate Change 16
Adaptation Practices in Climate Change 17
C. Limitations of the Health Sector Study 17
D. Project Methods and Validation Sites 18
E. Climate scenarios from PAG-ASA 20
III. RESULTS OF THE STUDY 21
A. Vulnerability, Adaptability and Impact Assessment Tools 21
Vulnerability and Adaptation Framework 21
Potential Vulnerabilities In Relation To Climate Change in the Philippine Health Sector 22
Burden of Disease 24
Breteau Index for predicting Dengue outbreaks 33
Vulnerability Maps to precisely locate vulnerability hotspots 35
B. Climate Change and Health Impact Modeling 44
Climate Change and Health Impact Modeling: Statistical Models 44
1. Statistical Modeling Process: Estimating the current distribution and burden of climate-
sensitive diseases 56
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a. Disease Impact Modeling and Projected Disease Impacts in 2020 and 2050 and
Socioeconomic Impact 56
b. A Time Series Analysis of the Effect of Climate Change on the Incidence of Dengue
in the National Capital Region, 1995-2007 66
2. Estimating the future potential health impacts attributable to climate change 84
C. Outputs of the V&A Tools 89
1. Projected 2020 and 2050 Cases of Selected Diseases 89
2. Cost Implications of Disease Impacts in 2020 and 2050 95
D. Compendium of Good and Innovative Climate Change Adaptation Practices 106
1. Adaptation Options Derived from Literature Review 107
2. Field documented Innovative Climate Change Adaptation Options for the Health Sector 120
3. Prioritized Adaptation Options 122
E. Integrated Monitoring and Evaluation Framework (IM&EF) 127
How the framework was formed 128
Validation of the proposed CCA M&E framework and system for the health sector 130
PIDSR as the Centerpiece of the Integrated M&E Framework 135
Vulnerability assessment and its link to the M&E framework 141
Indicators for the proposed M&E framework 147
Reconfiguring the existing reporting system for PIDSR 152
IV. CONCLUSIONS 154
V. RECOMMENDATIONS 157
VI. BIBLIOGRAPHY 162
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List of Tables and Figures
Tables
Table 1 Vulnerability Map Dataset Requirements ................................................................................ 36
Table 2 Model specifications of disease cases as responses to climate change variables ................. 57
Table 3 Availability of Disease and Weather Data, Palawan, Rizal, Pangasinan, and NCR ................ 61
Table 4 Sample Coefficient Table ......................................................................................................... 64
Table 5 Incidence of Infectious Diseases, National Capital Region, 1993 to 2007 .............................. 67
Table 6 ARIMA Model Parameters to Predict the Number of Dengue Cases, 1995-2007 ................... 73
Table 7 Summary of p-values for Predicting the Number of Dengue Cases using Climate Factors .... 80
Table 8 Number of cases predicted by minimum temperature ............................................................. 80
Table 9 Projected Climate Change Data in 2020 and 2050 ................................................................. 89
Table 10 Dengue Cases Projection, 2020 ............................................................................................ 90
Table 11 Dengue Cases Projection, 2050 ............................................................................................ 91
Table 12 Cholera Cases Projection, 2020 ............................................................................................ 91
Table 13 Cholera Cases Projection, 2050 ............................................................................................ 92
Table 14 Malaria Cases Projection, 2020 ............................................................................................. 92
Table 15 Malaria Cases Projection, 2050 ............................................................................................. 93
Table 16 Projected Dengue Costs (PhP), NCR, 2020 ......................................................................... 97
Table 17 Projected Dengue Costs (PhP), NCR, 2050 .......................................................................... 98
Table 18 Cost of Preventive Measures of Dengue, NCR, 2020. .......................................................... 99
Table 19 Cost of Preventive Measures of Dengue, NCR, 2050. .......................................................... 99
Table 20 Net savings from preventing Dengue................................................................................... 100
Table 21 Projected Cost (PhP) of Malaria Cases, Palawan, 2020 ..................................................... 101
Table 22 Projected Cost (PhP) of Malaria Prevention, 2020, Palawan .............................................. 102
Table 23 Projected Costs of Malaria, Palawan, 2050 ......................................................................... 102
Table 24 Cost Analysis Projections of Malaria Control Strategies, Palawan, 2050 ............................ 103
Table 25 Net savings from preventive measures for malaria. ........................................................... 104
Table 26 Income Classification of Provinces ...................................................................................... 104
Table 27 Percentages of Disease Costs Over Provincial Annual Income. ......................................... 105
Table 28 Types of Adaptation to Climate Change .............................................................................. 109
Table 29 Examples of Primary and Secondary Adaptation Measures to Reduce Health Impacts ..... 110
Table 30 Adaptation Options to Reduce the Potential Health Impacts of Climate Change ................ 110
Table 31 Health Sector Vulnerabilities to Climate Change-related diseases, 2011 ........................... 114
Table 32 Adaptations to Climate Change-related Diseases in Palawan, Pangasinan and Rizal, 2011
.................................................................................................................................................... 115
Table 33 Adaptation Evaluation Decision Matrix ................................................................................ 124
Table 34 Adaptation Evaluation Decision Matrix based on Literature ................................................ 126
Table 35 Climate Change M&E Dashboard Indicators ....................................................................... 131
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Table 36 Matrix showing the Climate Change and Health V&A Assessment Flowchart .................... 143
Table 37 Matrix table showing the monitoring and evaluation principles, responsible actors and levels,
relevant tools and indicators to be collected ............................................................................... 145
Figures Figure 1 The Philippine Climate Change Response Framework ............................................................ 2
Figure 2 Health Sector Climate Change Vulnerability and Adaptation Framework .............................. 24
Figure 3 The Breteau Index .................................................................................................................. 34
Figure 4 Flood and landslide prone areas in Pangasinan .................................................................... 38
Figure 5 Pangasinan Provincial and Municipal boundaries rendered over the forest cover shapefile . 39
Figure 6 Sample Maps of Rizal Province in Acetate platform ............................................................... 41
Figure 7 Vulnerability to Summer Temperature .................................................................................... 42
Figure 8 Vulnerability to high rainfall and flood ..................................................................................... 43
Figure 9 Conceptual Framework for Disease Impact Modeling ............................................................ 46
Figure 10 Graphical Representation of Disease Impacts vs. Rainfall .................................................. 50
Figure 11 Matrix Showing the Correlations of Independent Variables ................................................. 53
Figure 12 Matrix Showing the R-square Value ..................................................................................... 53
Figure 13 ANOVA Matrix ....................................................................................................................... 54
Figure 14 Coefficients Matrix ................................................................................................................ 55
Figure 15 Dengue Cases, Projected Cases and CC Indicators ............................................................ 58
Figure 16 Malaria Cases, Projected Values and CC Indicators ............................................................ 58
Figure 17 Cholera Cases, Projected Values and CC Indicators ........................................................... 59
Figure 18 Leptospirosis Cases and CC Indicators ............................................................................... 59
Figure 19 Typhoid Cases and CC Indicators ........................................................................................ 60
Figure 20 Malaria Incidence: National Capital Region, 1993-2009 ...................................................... 69
Figure 21 Dengue Incidence: National Capital Region, 1993-2007...................................................... 69
Figure 22 Cholera Incidence: National Capital Region, 1992-2009...................................................... 70
Figure 23 Leptospirosis Incidence: National Capital Region, 1996-2009 ............................................. 70
Figure 24 Weather Elements and Dengue Incidence: National Capital Region, 1993-2007 ................ 72
Figure 25 Time Series Models of the Numbers of Dengue Cases ....................................................... 82
Figure 26 Integrated M&E Framework ................................................................................................ 127
Figure 27 Proposed modification of PIDSR reporting system in the light of Climate Change ............ 153
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Acronyms and Abbreviations Used
AEFI – Adverse Event Following Immunization
AHA – Aquino Health Agenda
BHS – Barangay Health Station
CC – Climate Change
CCA – Climate Change Adaptation
CHAWF – The five sectors executing the MDGF 1656 project, namely, Coastal, Health, Agriculture,
Water, and Forestry/Biodiversity
CHD – Centers for Health Development; the regional offices of DOH
CHO – City Health Office
CIMSiM – Computational Intelligence, Modeling and Simulation
COSMIC – Computer Software Management and Information Center
CRR – Climate Risk Reduction
DA – Department of Agriculture
DALY – Disability Adjusted Life Years
DENR – Department of Environment and Natural Resources
DENSiM – Dengue Simulation Model
DILG – Department of Interior and Local Government
DOH – Department of Health
DOST – Department of Science and Technology
DRR – Disaster Risk Reduction
DTI – Department of Trade and Industry
FASPO – Foreign-Assisted and Special Projects Office (under the DENR)
FHSIS – Field Health Service Information System
FMB – Forests management Bureau
GIS – Geographical Information System
HadCM2 – Hadley Center Climate Model 2
HEMS – Health Emergency Management Service
HIV – AIDS – Health Immunodeficiency Virus – Acquired Immune Deficiency Syndrome
IDO – Infectious Disease Office (under the DOH – NCDPC)
IHPDS – Institute of Health Policy and Development Studies (under the NIH)
IMS – Information Management Service
IPCC – Inter-governmental Panel on Climate Change
LGU – Local Government Unit
MARA / ARMA – Mapping Malaria Risk in Africa / Atlas du Risque de la Malaria en Afrique
M&E – Monitoring and Evaluation
MDGF – Millennium Development Goal Fund
MESU – Municipal Epidemiologic Surveillance Unit
MHO – Municipal Health Office
MIASMA – Modeling Framework for the Health Impact Assessment of Man-Induced Atmospheric
Changes
NCDPC – National Center for Disease Prevention and Control (under the DOH)
NEC – National Epidemiology Center (under the DOH)
NEDA – National Economic Development Authority
NESSS – National Epidemiologic Sentinel Surveillance System
NFSCC – National Framework Strategy for Climate Change
NIH – National Institutes of Health
PAGASA – Philippine Atmospheric, Geophysical and Astronomical Services Administration (under
the DOST)
PESU – Provincial Epidemiologic Surveillance Unit
PHO – Provincial Health Office
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PIDSR – Philippine Integrated Disease Surveillance and Response
REECS – Resources, Environment, and Economics Center for Studies, Inc.
RESU – Regional Epidemiologic Surveillance Unit
RHU – Rural Health Unit
SARS – Severe Acute Respiratory Syndrome
UNDP – United Nations Development Program
UP Manila – University of the Philippines Manila
UPM – CPH - University of the Philippines Manila – College of Public Health
V&A – Vulnerability and Adaptation
WHO – World Health Organization
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EXECUTIVE SUMMARY
It is now widely accepted that the earth is warming, due to the emissions of
greenhouse gases as a consequence of human activity. There is also evidence that current
trends in energy use, development and population growth will lead to continuing – and more
severe – climate change. According to WHO, climate change puts at risk the basic
determinants of health: clean air and water, sufficient food and adequate shelter. Climate
change moreover exacerbates challenges to infectious disease control. Many of the major
causes of death are highly climate sensitive especially in relation to temperature and rainfall,
including cholera and the diarrheal diseases, as well as diseases including malaria, dengue
and other infections that are vector borne (WHO, 2009).
As such, climate change now regarded as a significant and emerging threat to public
health (WHO, 2003). In addition, the latest document by the Intergovernmental Panel on
Climate Change (IPCC), reports that there is overwhelming evidence that human activities
are causing changes in the global climate and these have dire implications on human health.
Climate change is already contributing to the global burden of disease and this contribution
is expected to grow even more unless governments take specific actions to address the
impacts of climate change on health (WHO, 2009).
Health is a focus reflecting the combined impacts of climate change on the physical
environment, ecosystems, the economic environment and society. Long-term changes in
world climate may affect many requisites of good health – sufficient food, safe and adequate
drinking water, and secure dwelling. The current large-scale social and environmental
changes mean that we must assign a much higher priority to population health in the policy
debate on climate change (IDS, 2006).
Climate change will affect human health and well-being through a variety of
mechanisms. Climate change can adversely impact the availability of fresh water supplies,
and the efficiency of local sewerage systems. It is also likely to affect food security. Cereal
yields are expected to increase at high and mid latitudes but decrease at lower latitudes.
Changes in food production are likely to significantly affect health in Africa. In addition, the
distribution and seasonal transmission of several vector-borne infectious diseases (such as
malaria, dengue and schistosomiasis) may be affected by climate change. Altered
distribution of some vector species may be among the early signs of climate change that
may affect health. A change in world climate could increase the frequency and severity of
extreme weather events. The impacts on health of natural disasters are considerable – the
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number of people killed, injured or made homeless from such causes is increasing
alarmingly. The vulnerability of people living in risk-prone areas is an important contributor to
disaster casualties and damage. An increase in heatwaves (and possibly air pollution) will be
a problem in urban areas, where excess mortality and morbidity is currently observed during
hot weather episodes (IDS, 2006).
Climate change is a huge threat to all aspects of human development and
achievement of the Millennium Development Goals for poverty reduction. Until recently,
donor agencies, national and local layers of government, and non-governmental
organizations have paid little attention to the risks and uncertainties associated with climate
change (IDS, 2006).
Stakeholders at all levels are increasingly engaging with the question of how to tackle
the impacts of climate change on development in poorer nations. There are growing efforts
to reduce negative impacts and seize opportunities by integrating climate change adaptation
into development planning, programmes and budgeting, a process known as mainstreaming.
Such a co-ordinated, integrated approach to adaptation is imperative in order to deal with the
scale and urgency of dealing with climate change impacts.
Out of the eight Millennium Development Goals (MDGs), four directly affect the
health sector. These include MDG 1: Eradicate extreme poverty and hunger, MDG 4:
Reduce child mortality, MDG 5: Improve maternal health and MDG 6: Combat HIV/AIDS,
malaria and other diseases. In the Philippines, MDG 5 has been identified as the most likely
not to be achieved target as the decrease of maternal deaths has been decreasing too
slowly to meet targets. Due to poor maternal health, child health is also in jeopardy unless
effective programs are in place. Since climate change has been identified to exacerbate the
effects of poor health, ineffective mitigation and adaptation are expected make maternal and
child health more fragile. Moreover, climate change is also expected to exacerbate vector
borne diseases such as malaria, dengue and leptospirosis. Related MDG goals especially
MDGs 4, 5 and 6 can be achieved by devising meaningful climate change adaptation
strategies that may be advanced by the results of this project.
Project Objectives
Generally, this project aimed to:
• develop a conceptual framework in the conduct of a vulnerability assessment and
impact modeling for the Public Health Sector;
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• develop the climate change vulnerability assessment framework for the Philippine
health sector;
• develop climate change monitoring and evaluation framework/system; and
• document good and innovative practices on climate change adaptation applicable to
the Health to be presented as a compendium of climate change adaptation best
practices in the Philippine Health sector.
This final report covers project outputs of the Health Sector component on
development of vulnerability assessment framework, including impact modeling and
socioeconomic projections. We discuss background literature that informed work on the
development of evolving frameworks. Results of the first and second consultative Round
Table Discussion (RTD) are presented to show comments on previous work that contributed
to vulnerability and adaptation framework development (See Book 2, Appendices B and D
for matrices). Adaptation best practices derived from literature and field visits are also
summarized together with an evolving list of literature that was reviewed for the project See
Book 3. Finally methods used in impact modeling for climate change and health are
discussed. A discussion on the approaches to mentoring colleagues from the Department of
Health on the conduct of the V&A and Climate Change socio-economic impact evaluation is
included in the final report. Proposed training materials are also included (See Book 4).
The project analyzed the relationship of incidences of selected climate-sensitive
diseases specifically, Malaria, Dengue, Leptospirosis, Cholera and Typhoid to changes in
certain climatic parameters. The project also looks into the current preparedness of health
services and systems in the event that an increase in disease magnitude or trend change is
observed. Project results include a description of the potential vulnerability or weaknesses
of the health sector in terms of adapting to the effects of climate change to the health
determinants. Project outcomes will ultimately assist in preparing the Health Sector to
effectively address the effects of Climate Change by coming up with a Vulnerability
Assessment Framework for the Health Sector, A Climate Change Monitoring and Evaluation
Framework for the Health Sector and a Compendium of applicable Climate Change
Adaptation Practices for the Health Sector.
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The final project report is contained in four (4) books. Book 1 contains the main
project report; while the report annexes are in book 2. The third book contains the
compendium of best climate change adaptation practices and book four presents the training
manuals that contain step by step procedures on how to utilize the V&A framework and the
integrated M&E framework.
Health Sector Climate Change Vulnerability and Adaptation Framework
Vulnerability assessment. Vulnerability to climate change-related diseases is a function of
several factors. These factors are classified into individual/family/community, health systems
and infrastructure, pathogen/vector factors, socio-economic factors, environmental factors,
and health/environmental policy. Specific indicators in each factor define the degree of
vulnerability of human population to the climate change-related diseases. The vulnerabilities
to climate change-related diseases are summarized in the following matrix indicating only
the highly vulnerable sector of the population.
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Vulnerabilities to Climate Change-related diseases
Vulnerability
Indicator
Dengue Malaria Leptospirosis Cholera Typhoid
Individual,
family,
community
Young and old
ages that are
exposed outdoor
activities during
dawn and dusk
with poor
sanitary practices
and facilities, low
immune system,
poor hygienic
practices, no
access to
sanitary water,
and lack of
health facilities
are highly
vulnerable
All ages with
poor sanitary
practices and
facilities, low
immune system,
poor hygienic
practices, no
access to
sanitary water,
lack of health
facilities are
highly
vulnerable.
All ages,
families,
communities
exposed in
flood-prone
areas where
population of
rats and
animals are
high are highly
vulnerable.
All ages
where water
systems are
easily
contaminated
with septic
waste
leakages
during floods
are highly
vulnerable.
All ages foods
and water
taken are
spoiled and
contaminated
are highly
vulnerable.
health
systems and
infrastructure
Highly vulnerable are those that have no access to clinics and hospitals and drug
stores including other important medical facilities.
pathogen/
vector factors
Communities and households environment that have no proper sanitation, no waste
management system, presence of canals and water bodies that are habitat of
pathogens and vectors are highly vulnerable
socio-
economic
factors
Highly vulnerable are the poor sector of the population. Those that are below poverty
income threshold level and cannot afford doctor‘s treatment as well as medicines.
environmental
factors
Highly vulnerable are communities close to bodies of stagnant water, unsanitary
environment, lack of waste management system, temperature, rainfall and relative
humidity favoring the growth of pathogens and vectors.
health/
environmental
policy
Highly vulnerable are communities and families not covered by policies on the regular
monitoring and treatment of diseases and maintenance of a sanitary environment.
Mapping each of the vulnerable indicators and overlaying them together identifies whether
an area, community, municipality or province is highly vulnerable, moderately vulnerable or
with low vulnerability. The vulnerability map evolving from the overlaying of the individual
maps representing the different vulnerability indicators guides the local government units in
allocating their resources for preventions and treatments of climate change-related diseases.
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Disease impact modeling. Reinforcing the vulnerable areas are the projections of potential
number of disease cases through the disease impact models developed out of existing
health and climate change data.
Assessment and evaluation of health and climate change data showed imperfect matching
and inadequacies causing major problems in developing the models. A remedial measure
adopted was to match projected climate change data from PAGASA in the four provinces
with health data. The health data were also found incomplete in terms of actual cases, alert
thresholds and epidemic thresholds. Also, adequate data on leptospirosis and typhoid is
wanting.
The models that passed statistical screening process and used in this study are:
a. Dengue Cases = -1267.347 - 0.615 * Monthly Rainfall - 21.389 * Maximum
Temperature + 31.442 * Relative Humidity
b. Cholera Cases = 8.948 + 0.026 * Monthly Rainfall - 1.681 * Maximum
Temperature + 0.663 * Relative Humidity
c. Malaria Cases = -218.918 - 0.089 * Monthly Rainfall + 7.605 Maximum
Temperature
Both dengue and cholera impact models were sensitive to monthly rainfall, maximum
temperature and relative humidity, whereas malaria is sensitive to monthly rainfall and
maximum temperature.
The models mean that for every unit of the variables, the corresponding responses of
disease cases are summarized on the table below:
Model specifications of disease cases as responses to climate change variables
Disease
Cases
One unit each of the variables will increase or decrease disease per
thousand cases
Monthly Rainfall
(mm/day)
Maximum Temperature
(degree centigrade)
Relative Humidity
(%)
Increase Decrease Increase Decrease Increase Decrease
Dengue 615 21,389 31,442
Cholera 26 1,681 663
Malaria 89 7,605
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Costs of Dengue. There are projected 1,735 cases of dengue in NCR in 2020. This will
require a total cost of PhP7.59 Mil for diagnosis, treatment cost of PhP4.29 Mil and a total
income loss of PhP2.11 Mil from families that will be affected.. The total cost of dengue will
be PhP13.99 Mil. If prevention measures will be done, the cost is estimated at PhP2.8 Mil,
which will give the LGU a net saving of PhP11.19 Mil.
In 2050, there will be 2128 dengue cases. the cost is PhP43.6 Mil. If preventive measures
will be implemented with a cost of PhP8.11Mil, the net saving is PhP35.51 Mil.
Costs of Malaria. The potential number of cases of malaria in 2020 is 187. The total fund
required (total cost for diagnosis plus the total cost of treatment) is PhP0.68 Mil. The cost of
preventing malaria is PhP0.28 Mil. This will give a saving of PhP0.4 Mil for the LGU.
In 2050, there will be 185 malaria cases. This will require a total budget of PhP2.38 M for the
diagnoses and treatments. The prevention cost of malaria is PhP1.0 Mil. Doing the
preventive measures will save PhP1.38 Mil for the LGU.
Cost of Leptospirosis, Cholera and Typhoid. There were no cost data on the diagnoses,
treatments, income losses and costs of prevention for leptospirosis, cholera, and typhoid in
NCR, Palawan, Pangasinan, and Rizal. Thus, no economic analyses were done.
Adaptation Practices. The main objective of this activity were to identify policy options and
measures on climate change adaptation measures on health that suit Philippine setting and
the integration of these measures for national and local development planning processes.
These outputs were obtained through an intensive literature review, as well as consultation
with experts on the various diseases and meetings with representatives of health agencies.
Validation was undertaken through the visits in 3 selected provinces namely Palawan, Rizal
and Pangasinan.
Experiences with the different adaptation practices where they have been implemented were
gathered through an extensive search of related literature from the internet and through
presentations of experts on the various diseases. These practices are being assessed and
attempts are also being made to explain successes and failures, especially the factors that
contributed to them. The adaptation strategy were categorized to address the vulnerabilities
identified in the V&A framework, comprising of the following: (a) individual/family/community;
(b) health system and infrastructure; (c) pathogen and vector factors; (d) socio-economic
factors; (e) environmental factors; and (f) health and environmental policy. The adaptation
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measures must incorporate capacitating the individual, family, and/or community to analyze
and adequately respond to future climate risks.
Based on consultations with health workers in Palawan, Pangasinan and Rizal, the
adaptation measures that they use to prevent and control the spread of dengue, malaria,
leptospirosis, cholera and typhoid are summarized in the following matrix.
Adaptations to Climate Change-related Diseases in Palawan, Pangasinan and Rizal
Adaptation
Practices
Dengue Malaria Leptospirosis Cholera Typhoid
Individuals and Family
Use of treated or
untreated
mosquito nets.
Proper use of
nets at the
right time and
right place
Proper use of
nets at the right
time and right
place
Provision of
screens and
sealing of holes in
houses
Prevent
entries of
mosquitoes
Prevent entries
of mosquitoes
Cleanliness of
immediate
household‘s
surroundings
Removal of
waters in
containers
inside and
outside the
house
Removal of
breeding
grounds of
mosquitoes
inside and
outside the
house by
practicing
proper waste
disposal
Elimination of
damp areas
conducive for
rat‘s habitat.
Clean drainage
system most
often to prevent
breeding
grounds of rats
Water, sanitation
and good hygienic
practices
Source out
water for
drinking that are
safe or free from
contamination.
Sterilize water
before drinking.
Store foods
properly
avoiding
contacts with
probable
carriers of
cholera. Practice
sanitation and
Same as
cholera
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Adaptation
Practices
Dengue Malaria Leptospirosis Cholera Typhoid
good hygiene in
the family.
Consciousness on
good health
maintenance
Early diagnosis and treatment of climate change-related diseases.
Maintenance of
pets at home that
can reduce growth
of vectors and
pathogens
Breeding of larvivarous species
of fish
Maintenance of
cats that feed
on rats
Barangay or
Community
Presence of active
barangay health
workers.
Report
suspected
cases to
hospitals for
immediate
diagnosis
and
treatment
Microscopists
for malaria only
for immediate
diagnosis and
treatment of
climate
change-health
related
diseases
Report cases of
suspected
infected
persons for
treatment
Refer cases to
hospitals for
diagnosis and
treatment
Refer cases
to hospitals
for
immediate
diagnosis
and
treatment
Decanting Spraying
pesticides
that are not
toxic to
human being
Destroy rats
and breeding
grounds and
habitat
Provision of a
centralized clean
water sources that
are well protected
and maintained
whole year round
Spring development; Prevention
of water source contamination
by sealing potential entry of
pathogens/vectors.
Community
ordinances: zoning
and resettlement
of high risk groups
or informal
settlers.
Resettlement
in dengue-
free zones.
Resettlement in
malaria free
zone.
Resettlement in
elevated and
non-flood prone
areas.
Remove sources of water
contamination or resettlement
contaminated groups.
Proper waste
management
system at
community level
Removal of wastes that
promote growth of
pathogens/vectors; cleaning of
water ways
Eliminate
breeding
grounds of rats
Removal of
sources of
contamination;
location of water
sources away
Prevent
sources of
pathogens/
vectors
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Adaptation
Practices
Dengue Malaria Leptospirosis Cholera Typhoid
from
sewage/waste
dumping areas.
dengue
coming from
waste/sewa
ge areas.
Presence of
manned and active
health workers in
BHCs.
Regular diagnosis, treatment and referrals/endorsement to hospitals that can treat
diseases.
Information and
Education
Campaign at the
Community Level
Barangay Health Centers with regular information campaign activities for the
barangay population regarding prevention adaptation measures of all diseases
Health systems and infrastructure
Presence of a
network of
complimentary
hospitals complete
with laboratory,
medicines, and
medical facilities
within the province
where costs of
diagnosis and
treatments are
affordable.
Conduct thorough diagnoses and treatments of infected persons.
Health care
system
PhilHealth card necessary for each family
Holistic health
maintenance
projects
Fourmula-1,
Vaccination,
PIDSR, etc.
Fourmula-1,
PIDSR, malaria
treatment
medicines, etc.
Fourmula 1,
PIDSR,
leptospirosis
treatment
medicines
Fourmula 1,
PIDSR, cholera
treatment
medicines
Fourmula 1,
PIDSR,
typhoid
treatment
medicines
Pathogen/vector factors
Innovative
practices to
eliminate vectors
and pathogens.
Solar insecticide capture and
destroy
Rats trapping Floating Toilet
Device
Floating
Toilet
Device
xx
Adaptation
Practices
Dengue Malaria Leptospirosis Cholera Typhoid
Regular spraying
of chemicals that
are non- toxic to
human beings to
eliminate
pathogens and
vectors inside and
outside the house.
Regular and simultaneous
spraying that kills mosquitoes
and other insects, fungi and
other pathogens in all houses
and breeding grounds in a
barangay.
Regular and
simultaneous
decanting by
barangay level.
Elimination of
growth factors and
habitat.
Cleaning of water ways,
streams and other water
bodies, and proper sanitary
practices at the household
level.
Cleaning of
canals; removal
of rat habitat
and wastes.
Avoid food spoilage by
refrigeration and maintenance
of clean and safe water
sources.
Socio-economic Factors
Health subsidies
in vulnerable
communities or
barangays.
Subsidies to all vulnerable families in the form of free or affordable health card.
Provision of
livelihood and
income generating
projects to
increase income of
vulnerable
communities.
Planting, processing and marketing of medicinal plants proven to strengthen immune
system; manufacture and marketing of decanting and trap gadgets; production and
marketing of insect repellants; production, breeding and marketing of pets that feed
on insects and rats.
PPP for clean and
safe water system
Replace old
water system
vulnerable to
contamination
Replace old
water
system
vulnerable to
contaminatio
n.
Environmental factors
Forestation Planting and
management
of integrated
forest
plantations
that drive
away
mosquitoes
Planting and
management of
integrated
forest
plantations that
drive away
mosquitoes
Planting of
forest species
that attract rats
from residential
areas.
Establishment,
maintenance and
management of
Eliminates breeding grounds of
insects, pathogens and vectors.
Eliminates
breeding
Eliminates breeding grounds of
insects, pathogens and vectors.
xxi
Adaptation
Practices
Dengue Malaria Leptospirosis Cholera Typhoid
sanitary landfills. grounds of rats.
Periodic cleaning
and declogging of
waterways,
streams and rivers
to allow waterflow
continuously.
Breeding grounds of
mosquitoes in stagnant water
are destroyed.
Flowing
streamflow
prevent s
depostion of
wastes for rat
foods .
Clean and declogged
waterways also washout
pathogens and vectors that live
on stagnant water.
Health/ environmental policy
Policy on the
integration of
health and climate
change education
in primary and
secondary
education
Education on
the
prevention of
dengue at
home and in
school .
Education on
the prevention
of malaria at
home and in
school.
Education on
the prevention
of leptospirosis
Education on
the prevention of
cholera
Education
on the
prevention
of typhoid
Climate risk
proofing policies
Adoption and implementation of adaptation measures for climate change-related
health problems in all DOH projects
Policy on
mandatory
coverage of
population for
health care system
Full coverage in highly vulnerable areas.
Policy on
Strengthened
Provincial Disaster
Coordinating
Council
Creation of a sub-council on disease-related disaster prevention and management
Disaster
preparedness
policy.
Nationwide capacity building of people on disaster preparedness brought about by
climate change-related diseases.
Monitoring and Evaluation (M&E) Framework. This is an offshoot of existing DOH-
PIDSAR, NGAs, and at the international level, the UNDP-GEF-M&E, and M&E of the
International Federation of Red Cross and Red Crescent Societies. These M&Es monitor
climate change adaptation and climate change programs.
The schematic diagram of the Integrated M&E for health under climate change conditions is
shown in the following figure.
xxii
Integrated M&E Framework
The components of the M&E framework are the climate change indicators, the vulnerability
factors, PIDSAR and the adaptation measures component. These components are
discussed in the major components.
Conclusions
A Vulnerability and Adaptation Impact Assessment Framework for the health sector was
devised for this project and grounded the results and outputs of the study. The research
team utilized this framework to test the other deliverables of the study and found that the
framework works.
The categories of vulnerabilities that were culled from the review of literature and the round
table discussion provided focus and specificity to the vulnerability assessment and
adaptation documentation. Hence the team deems it is safe to be recommended for use in
the country. While utilizable, the team avers that the framework can still be refined through
xxiii
pilot tests of the framework and its parts in different areas of the country and for various
usages.
The following section provides more specific conclusions from the applications of the
vulnerability assessment models that were derived in the project.
1. Disease impact models for dengue, malaria, and cholera were developed out of available
data from the NCR and PHOs of Palawan, Pangasinan and Rizal. The robustness of the
models depend on the accuracy of the health and climate data measurements or
estimations. Leptospirosis and typhoid impact models were not formulated due to
inadequate data.Disease impacts for 2020 and 2050 were conducted using the projection
models that passed the statistical screening process. Refinements of the models may be
done as additional data on health and climate change are made available.
2. Assessment and evaluation of health data showed purely medical-related data and no
climate change data. This was a major problem. A remedial measure adopted was to match
climate change data from PAGASA in the NCR, Palawan, Pangasinan and Rizal. The health
data were also found incomplete in leptospirosis and typhoid. Also, disease cases were not
available in Palawan, Pangasinan and Rizal. This could be due to lack of real disease
occurrence or with disease occurrence but no documentation. Likewise, in NCR, only
disease cases were available.
The Time Series Analysis also provided important insights into the climate change and
impact assessments:
3. Consistent results that the observed minimum temperatures for the current month provide
the most significant positive contributions to the model to predict the number of dengue
cases for any month of observation.
4. A peak of dengue incidence occurs thereafter in about a month after the start of the
increase of cases. This coincides with the years when a surge of cases was
experienced in NCR (as was mentioned previously: 1996, 1998, 2001, 2005 and 2006).
(This is evident as crests of maximum temperature seem to frequently transpire a little
earlier compared to the peaks of minimum temperature. This would be consistent with
the lack of significance in estimates for dengue cases based on maximum
temperature.)
xxiv
Economic impact analyses were accomplished for dengue and malaria. Other diseases
such as leptospirosis, cholera, and typhoid were not covered in the economic analysis due to
lack of data. Rizal was not also covered because of lack of both climate change and
economic data on the selected diseases.
5. The results in NCR and Palawan indicate that malaria and dengue in 2020 would require
about 1% of its annual income for the diagnoses and treatments of malaria and dengue. In
2050, the allocation for funding for the same diseases would reach no less than 2% of the
provincial income.
6. However, Pangasinan in 2020 would need about 18% of its income only for diagnoses
and treatments of malaria and dengue. In 2050, the budget requirement for both diseases
would be reduced to 4% owing to the reduced number of malaria and dengue cases, which
is not attributed to preventive measures that would be implemented, but to the changes in
the climate indicators. Such climate change nonetheless, may be good from the point of view
of reducing disease occurrence.
7. Considering the five diseases for budgeting purposes, the provincial government may
allocate in the future roughly a total of 2.5% of the income of Palawan in 2020 and 5% in
2050 assuming that the average cost requirement of each disease would more or less be the
same with malaria and or dengue. On the other hand, Pangasinan would allocate roughly
45% of its income in 2020 to address the five diseases and 20% in 2050.
8. Considering the substantial savings that could be generated from applying preventive
measures, the two provincial governments may consider investing on financing preventive
measures to lessen the cost impacts of the diseases, thus lessen the burden of the
provincial governments in addressing these diseases.
9. Provincial and municipal governments should not wait for the diseases to reach epidemic
levels before they address the malaria and dengue outbreaks as well as other diseases that
would emerge and be aggravated by climate change conditions. It is most certainly
beneficial to prevent disease outbreaks before they even emerge.
10. Applying effective preventive measures against dengue would result in significant
savings on the part of the provincial government in the amounts of PhP 10.9 M in 2020 and
PhP 31.69 M in 2050.
xxv
Recommendations
1. The most critical recommendation that this study provides is that there is a need to
improve the data bases and information systems that feeds into climate change vulnerability
and adaptation assessment for the health sector. Currently, there are no linked data
between health outcomes and meteorological information. Timely disease surveillance and
case finding may be triggered by accurate weather and climate information that should be
provided to health and LGU managers at all levels.
Governments should engage more actively with the scientific community, who in turn
must be supported to provide easily accessible climate risk information.
Climate risk information should put current and future climate in the perspective of national
development priorities.
Information needs of different actors should be considered and communication tailored more
specifically to users, including the development community
2. Another major recommendation is to create systems to strengthen mainstreaming
adaptation within existing poverty alleviation policy frameworks. There is a lack of research
on the extent to which climate change, and environmental issues more broadly, have been
integrated within national policy and planning frameworks. National Adaptation Programmes
of Action or NAPA was a project funded by the Least Development Countries Fund (LDC
Fund) and commissioned by the UNFCCC to the 48 least developed countries need to be
utilized for this purpose.
This is critical. Examples of efforts from Sri Lanka, Bangladesh, Tanzania, Uganda, Sudan,
Mexico and Kenya are presented, highlighting a number of key issues relating to current
experiences of integrating climate change into poverty reduction efforts (IDS, 2006).
As previously discussed climate change stakeholders at all levels are increasingly engaging
with the question of how to tackle the impacts of climate change on development in poorer
nations. There are growing efforts to reduce negative impacts and seize opportunities by
integrating climate change adaptation into development planning, programmes and
budgeting, a process known as mainstreaming. Such a co-ordinated, integrated approach to
adaptation is imperative in order to deal with the scale and urgency of dealing with climate
change impacts (IDS, 2006).
xxvi
In developed countries progress on mainstreaming climate adaptation has been limited.
Many countries have carried out climate change projections and impact assessments, but
few have started consultation processes to look at adaptation options and identify policy
responses.
The following section provides more detailed recommendations for specific research outputs:
On Impact modeling
For better disease impact modeling, the following are strongly recommended:
Improve existing database on health and climate change through standardization of health
and climate change data monitoring forms and that data gathering should be localized. Since
the occurrences of diseases are localized and climate change variations are also localized,
there is a need to strengthen the PHO and LGU in each province on health and climate
change indicators monitoring, modeling, and analysis. The reason for this is to enable the
health sector for immediate response to address climate change health-related problems
without waiting decisions from the national level.
Basic weather instrumentation set up containing rain gauge, thermometer, evaporation pan,
relative humidity measurer, wind velocity meter may be funded out of the IRAs of each of the
provinces and may be installed in the municipalities. This is important to capacitate the
municipal LGUs and provincial PHOs on health and climate change concerns so that
immediate adaptation measures can be implemented right where the problems are.
Intensify research on the environmental habitat of disease vectors including the climate
change conditions favoring their growth and their life cycles. Determine to what extent does
the vector live and in what conditions. Identify the types of vector, where they are and
estimate vector population so that proper strategies to control their spread may be
implemented without waiting for an outbreak. Knowing the vulnerable areas by barangay
would be a significant step in controlling diseases. In modeling, there is a need to include
environmental variables that favor the occurrences of disease vectors and their population in
addition to the climatic factors
Vulnerability assessment at the barangay level should be mapped for effective
implementation of adaptation measures.
The models are not advisable for national application due to differences in the environmental
conditions and climatic change factors in each of the provinces. Averaging provincial data
would result in substantial discrepancies between predicted values and actual disease
xxvii
observations due to substantial inter-provincial variations on climatic and environmental
conditions. The only remedy is for each province to develop its own impact models for
climate change-sensitive diseases.
In order to address the threats of the climate change-related diseases, preventive measures
were recommended for implementation for malaria and dengue control. Thus, the provincial
governments are encouraged to fund such preventive measures to restrict the possible
spread of the diseases. The effective implementation of the preventive measures will result
in substantial savings from the income of the provincial government.
Other recommendations that would help the provincial governments to become more
responsive in addressing the threats of climate change-related diseases include the
following:
7. Conduct of studies on economic impacts of other climate change-related diseases such
as leptospirosis, cholera, and typhoid in Palawan and Pangasinan.
8. Conduct of studies of economic impacts of malaria, dengue, leptospirosis, cholera, and
typhoid in Rizal Province and other provinces that are vulnerable to climate change.
9. Pursue studies on the costs of other adaptation measures on health to minimize or control
the impacts of climate change-related diseases.
Recommendations from time Series Analysis
10. Regarding the generalizability of the results of the models developed, though the data
analysis had solely used data from cities of NCR, it would be potentially useful to also
apply the results to other LGUs elsewhere (i.e. other urban areas) where communities
have experienced outbreaks of dengue in the past. It is therefore critical that local health
officials should work closely with national health authorities to coordinate efforts in mitigating
the effects of a rise in temperature (i.e. recorded minimum temperature) on a possible
increase in dengue cases and/or the occurrence of dengue outbreaks particularly during
periods when an occurrence of an El Niño/La Niña –Southern Oscillation (ENSO) condition
in the Pacific Ocean is announced and experienced.
Policy Recommendations
Policy recommendations have largely been culled from the literature as a full policy analysis
for climate change and health was not feasible within the project. International experiences
xxviii
so far highlight a number of barriers and opportunities to mainstreaming climate change
adaptation in developing countries. These are focused around information, institutions,
inclusion, incentives and international finance, and result in a number of recommendations
for national governments (IDS, 2006).
The following section contains those recommendations deemed appropriate for the
Philippines.
Recommendations for stakeholders:
A multi-stakeholder coordination committee should be established to manage national
adaptation strategies, chaired by a senior ministry.
Regulatory issues should be considered from the start of the mainstreaming process.
The capacity of existing poverty reduction mechanisms is consistent with existing policy
criteria, development objectives and management structures.
Policy-makers should look for policies that address current vulnerabilities and development
needs, as well as potential climate risks.
Actions to address vulnerability to climate change should be pursued through social
development, service provision and improved natural resource management practices.
A broad range of stakeholders should be involved in climate change policy-making, including
civil society, sectoral departments and senior policy-makers.
Climate change adaptation should be informed by successful ground-level experiences in
vulnerability reduction.
NGOs should play a dominant role in building awareness and capacity at the local level.
Recommendations for incentives:
Donors should provide incentives for developing country governments to take particular
adaptation actions, appropriate to local contexts.
The economic case for different adaptation options should be communicated widely.
A risk-based approach to adaptation should be adopted, informed by bottom-up
experiences of vulnerability and existing responses.
xxix
Approaches to disaster risk reduction and climate change adaptation should be merged in a
single framework, using shared tools.
Finally, the major results of the study can be summarized into the Health Sector Climate
Change Vulnerability and Adaptation Matrix which follows:
Health Sector Climate Change Vulnerability and Adaptation Matrix
Area/Sector (C, H, A, W, F)
CC Vulnerability Socio-Economic
Impact Adaptation
Option/Activities
HEALTH Climate Sensitive
Diseases Projections
Malaria is projected to have 258 new cases by 2020, and 308 new cases by 2050.
By 2020, there will be 143 new cases of cholera. In 2050, there will be 99 new cases of cholera.
Dengue cases in NCR will amount to 2,128 by 2020, and 1,735 cases in 2050.
(No projections were made for leptospirosis and typhoid fever due to the lack of models.)
Individual, family,
community
Poor sanitary practices and facilities
Low immune system
Poor hygienic practices
Poor access to potable water
No access to health facilities
Exposure to vectors,
Malaria
For every unit of monthly rainfall, malaria will be reduced by 89 per 1,000 cases.
Cholera
For every unit of monthly rainfall, cholera cases will increase by 26 per 1,000 cases.
For every unit of maximum temperature, cholera cases will be increased by nearly 8 cases.
For every unit of maximum temperature, cholera cases will decline by almost 2 cases.
For every unit of relative humidity, cholera cases will increase by 662 per 1,000 cases.
Dengue
For every unit of
Construction of climate resistant houses
Ensure adequate supply of potable water: SODIS, SCW System
Regular house-spraying
Provision of insecticide-treated bed nets in Malaria endemic areas
Improve household sanitation
Floating Toilet Device
xxx
Area/Sector (C, H, A, W, F)
CC Vulnerability Socio-Economic
Impact Adaptation
Option/Activities contaminated water and food
Do not have climate change resistant shelters
Live in disaster prone areas i.e. flood plains or watershed slopes
monthly rainfall, dengue cases will decline by 615 per 1,000 cases.
In NCR, for every 1° C increase in recorded minimum temperature, an expected 233 cases of dengue is predicted to occur.
For every unit of maximum temperature, dengue will decrease by about 21 cases.
For every unit of relative humidity, dengue cases will rise by about 31 cases.
By 2020, the total cost of diagnosis & treatment of dengue and malaria as a percentage of annual provincial income will be 0.15% for Palawan, and 7.21% for Pangasinan.
By 2050, the total cost of diagnosis & treatment of dengue and malaria as a percentage of annual provincial income will be 0.53% for Palawan, and 18.03% for Pangasinan.
For every 1°C increase in temperature, the mosquito population increases ten-fold (east African highlands, 2009)
Increased bite rate of
Health Systems and
Infrastructure
Inequitable
distribution of health
system factors i.e.
clinics, hospitals,
pharmacies and
human resources for
health (HRH) that
lead to population‘s
lack of access to
quality basic health
services
Health information
system not related to
climate change leads
to difficulty in
monitoring climate
change related
illnesses
Disease prevention
and health promotion
systems weak
Inability to respond properly & quickly to emergency/disaster situations
Implementation of education campaigns to eliminate breeding sites
Adoption of a risk-based approach to adaptation
Improved disease monitoring and surveillance systems
Early case detection and improved case management
Pathogen/vector factors
Poor sanitation
facilities and systems
Solid waste
management systems
below standard
Presence of vector
habitats (e.g. canals
and bodies of water)
Release of sterile male vectors
Introduction of larvivarous fish in natural and artificial ponds and wetlands
xxxi
Area/Sector (C, H, A, W, F)
CC Vulnerability Socio-Economic
Impact Adaptation
Option/Activities Socio-economic factors
Below poverty threshold level
Unable to afford or sustain recommended treatment
mosquitoes with increased temperature
Contamination of water sources
the potential impacts of climate change would be US$5–19 million by 2050 in terms of loss of public safety, increased vector- and waterborne diseases, and
increased malnutrition from food shortages during extreme events (Fiji, WHO 2003)
Provision of a National disaster insurance fund
Environmental factors
Human proximity to vector habitats (e.g. canals, bodies of water)
Temperature, rainfall and relative humidity favors vector and pathogen growth
Integrated water management
Weather forecasting and early warning systems
Water disinfection through the use of solar power (e.g. SCW System, SODIS)
Integrated environmental management
Health/environmental policy
Lack of policies on regular monitoring and treatment of diseases
Lack of policies on maintenance of a sanitary environment
Lack of effective policies on
Weak implementation of existing policies on disease control.
Ban on precarious residential placements
Land zoning restrictions based on hydrological and risk assessment studies
Establishment of a multi-stakeholder coordination committee to manage national adaptation strategies
Use of radio and television for information dissemination
Mainstreaming of Climate Change into government policies
1
I. INTRODUCTION
It is now widely accepted that the earth is warming, due to the emissions of
greenhouse gases as a consequence of human activity. There is also evidence that current
trends in energy use, development and population growth will lead to continuing – and more
severe – climate change. According to WHO, climate change puts at risk the basic
determinants of health: clean air and water, sufficient food and adequate shelter. Climate
change moreover exacerbates challenges to infectious disease control. Many of the major
causes of death are highly climate sensitive especially in relation to temperature and rainfall,
including cholera and the diarrheal diseases, as well as diseases including malaria, dengue
and other infections that are vector borne (WHO, 2009).
As such, climate change now regarded as a significant and emerging threat to public
health (WHO, 2003). In addition, the latest document by the Intergovernmental Panel on
Climate Change (IPCC), reports that there is overwhelming evidence that human activities
are causing changes in the global climate and these have dire implications on human health.
Climate change is already contributing to the global burden of disease and this contribution
is expected to grow even more unless governments take specific actions to address the
impacts of climate change on health (WHO, 2009).
Health is a focus reflecting the combined impacts of climate change on the physical
environment, ecosystems, the economic environment and society. Long-term changes in
world climate may affect many requisites of good health – sufficient food, safe and adequate
drinking water, and secure dwelling. The current large-scale social and environmental
changes mean that we must assign a much higher priority to population health in the policy
debate on climate change (IDS, 2006).
Climate change will affect human health and well-being through a variety of
mechanisms. Climate change can adversely impact the availability of fresh water supplies,
and the efficiency of local sewerage systems. It is also likely to affect food security. Cereal
yields are expected to increase at high and mid latitudes but decrease at lower latitudes.
Changes in food production are likely to significantly affect health in Africa. In addition, the
distribution and seasonal transmission of several vector-borne infectious diseases (such as
malaria, dengue and schistosomiasis) may be affected by climate change. Altered
distribution of some vector species may be among the early signs of climate change that
may affect health. A change in world climate could increase the frequency and severity of
extreme weather events. The impacts on health of natural disasters are considerable – the
number of people killed, injured or made homeless from such causes is increasing
2
alarmingly. The vulnerability of people living in risk-prone areas is an important contributor to
disaster casualties and damage. An increase in heatwaves (and possibly air pollution) will be
a problem in urban areas, where excess mortality and morbidity is currently observed during
hot weather episodes (IDS, 2006).
The effects of Climate Change have always been linked to recent disasters in the
Philippines. Catastrophic phenomena such as El Niño, La Niña, and severe flooding have
taken numerous lives and destroyed properties. Consequently, these events have
enlightened our country and the international community into recognizing that Climate
Change is real and now and thus should be demystified with appropriate attention.
From the time climate change has taken the limelight in the world stage, much effort
and financial resources have already been spent and channeled worldwide. In the
Philippines, the government has already started putting up action plans, mitigation and
adaptation strategies to address the impacts of climate change. However, even with all
these efforts, not much focus has been given to the health sector as evidenced by the
inadequacy of available research and literature on the climate change and health and the
inadequacy of action plans to address climate change impacts on health directly.
Figure 1 The Philippine Climate Change Response Framework
(Source: Presidential Task Force on Climate Change)
3
The Department of Health of the Philippines has started laying out plans in
addressing the aforementioned concerns on climate change and health in accordance with
the Philippine Climate change Response Framework as shown in Figure 1. A national policy
on Climate Change mitigation and adaptation has already been enacted. However, the
mitigation and adaptation strategies that will effectively address climate change needs to be
integrated and multi-sectoral while being culturally relevant and appropriate to the resource
availability within the different regions. This is also the case in other nations of the world.
Hence, the United Nations and Government of Spain implemented a joint program in the
country that was aimed at strengthening the Philippines‘ institutional capacity to adapt to
climate change. This program focused on five priority sectors including Coastal, Health,
Agriculture, Water, and Forestry/Biodiversity (CHAWF). The program sought to mainstream
climate risk adaptation into key national & local development planning & regulatory
processes, enhance capacities of key partners and communities to undertake climate
resilient development, and test integrated adaptation approaches with up scaling potential.
The program brought together GOP and UN partners over a period of 3 years to complete
the country‘s knowledge base and strengthen institutional capacities to manage climate
change risks. The University of the Philippines Manila, through the Institute of Health Policy
and Development Studies, was one of the UP units that were commissioned to undertake
the present study to represent the health sector.
Health Millennium Development Goals
Climate change is a huge threat to all aspects of human development and
achievement of the Millennium Development Goals for poverty reduction. Until recently,
donor agencies, national and local layers of government, and non-governmental
organizations have paid little attention to the risks and uncertainties associated with climate
change (IDS, 2006).
Now, however, players at all levels are increasingly engaging with the question of
how to tackle the impacts of climate change on development in poorer nations. There are
growing efforts to reduce negative impacts and seize opportunities by integrating climate
change adaptation into development planning, programmes and budgeting, a process known
as mainstreaming. Such a coordinated, integrated approach to adaptation is imperative in
order to deal with the scale and urgency of dealing with climate change impacts.
4
In developed countries progress on mainstreaming climate adaptation has been
limited. Many countries have carried out climate change projections and impact
assessments, but few have started consultation processes to look at adaptation options and
identify policy responses.
In developing countries, the mainstreaming process is also in its early stages. Small
island developing states have made good progress, with Caribbean countries among the first
to start work on adaptation. The Pacific islands have received considerable support and
through the World Bank a number of initiatives have begun.
Crucially, there has been little progress in mainstreaming adaptation within existing
poverty alleviation policy frameworks. There is a lack of research on the extent to which
climate change, and environmental issues more broadly, have been integrated within
PRSPs. This is critical. Examples of efforts from Sri Lanka, Bangladesh, Tanzania, Uganda,
Sudan, Mexico and Kenya are presented, highlighting a number of key issues relating to
current experiences of integrating climate change into poverty reduction efforts.
Out of the eight Millennium Development Goals (MDGs), four directly affect the
health sector. These include MDG 1: Eradicate extreme poverty and hunger, MDG 4:
Reduce child mortality, MDG 5: Improve maternal health and MDG 6: Combat HIV/AIDS,
malaria and other diseases. In the Philippines, MDG 5 has been identified as the most likely
not to be achieved target as the decrease of maternal deaths has been decreasing too
slowly to meet targets. Due to poor maternal health, child health is also in jeopardy unless
effective programs are in place. Since climate change has been identified to exacerbate the
effects of poor health, ineffective mitigation and adaptation are expected make maternal and
child health more fragile. Moreover, climate change is also expected to exacerbate vector
borne diseases such as malaria, dengue and leptospirosis. Related MDG goals especially
MDGs 4, 5 and 6 can be achieved by devising meaningful climate change adaptation
strategies that may be advanced by the results of this project.
Project Objectives
Generally, this project aimed to:
1. develop a conceptual framework in the conduct of a vulnerability assessment and
impact modeling for the Public Health Sector;
2. develop the climate change vulnerability assessment framework for the Philippine
health sector;
5
3. develop climate change monitoring and evaluation framework/system; and
4. document good and innovative practices on climate change adaptation applicable to
the Health to be presented as a compendium of climate change adaptation best
practices in the Philippine Health sector.
This final report covers project outputs of the Health Sector component on
development of vulnerability assessment framework, including impact modeling and
socioeconomic projections. We discuss background literature that informed work on the
development of evolving frameworks. Results of the first and second consultative Round
Table Discussion (RTD) are presented to show comments on previous work that contributed
to vulnerability and adaptation framework development (See Book 2, Appendices B and D
for matrices). Adaptation best practices derived from literature and field visits are also
summarized together with an evolving list of literature that was reviewed for the project (See
Book 3). Finally methods used in impact modeling for climate change and health are
discussed. A discussion on the approaches to mentoring colleagues from the Department of
Health on the conduct of the V&A and Climate Change socio-economic impact evaluation is
included in the final report. Proposed training materials are also included (See Book 4).
The project analyzed the relationship of incidences of selected climate-sensitive
diseases specifically, Malaria, Dengue, Leptospirosis, Cholera and Typhoid to changes in
certain climatic parameters. The project also looks into the current preparedness of health
services and systems in the event that an increase in disease magnitude or trend change is
observed. Project results include a description of the potential vulnerability or weaknesses
of the health sector in terms of adapting to the effects of climate change to the health
determinants. Project outcomes will ultimately assist in preparing the Health Sector to
effectively address the effects of Climate Change by coming up with a Vulnerability
Assessment Framework for the Health Sector, A Climate Change Monitoring and Evaluation
Framework for the Health Sector and a Compendium of applicable Climate Change
Adaptation Practices for the Health Sector.
The final project report is contained in four (4) books. Book 1 contains the main
project report; while the report annexes are in Book 2. The third book contains the
compendium of best climate change adaptation practices and book four presents the training
manuals that contain step by step procedures on how to utilize the V&A framework and the
integrated M&E framework.
6
II. CONCEPTUAL AND PROCESS FRAMEWORK OF THE
HEALTH SECTOR STUDY
A. Concepts and Terminologies
The World Health Organization (WHO) reports that climatic changes over recent
decades have probably already affected some health outcomes. The World Health Report
2002 says that climate change was estimated to be responsible in year 2000 for
approximately 2.4% of worldwide diarrhea, and 6% of malaria in some middle-income
countries. The first detectable changes in human health may well be alterations in the
geographic range (latitude and altitude) and seasonality of certain infectious diseases –
including vector-borne infections such as malaria and dengue fever, and food-borne
infections which normally peak in the warmer months. Warmer average temperatures
combined with increased climatic variability would alter the pattern of exposure to thermal
extremes and resultant health impacts (WHO, 2002).
The Intergovernmental Panel on Climate Change (IPCC) concluded that overall,
climate change is projected to increase threats to human health, particularly in lower income
populations and predominantly within tropical/subtropical countries. Three broad categories
of health impacts are associated with climatic conditions: (1) impacts that are directly related
to weather/climate; (2) impacts that result from environmental changes that occur in
response to climatic change; and (3) impacts resulting from consequences of climate-
induced economic dislocation, environmental decline, and conflict. The first two categories
are often referred to as climate-sensitive diseases; these include changes in the frequency
and intensity of thermal extremes and extreme weather events (i.e., floods and droughts)
that directly affect population health, and indirect impacts that occur through changes in the
range and intensity of infectious diseases and food- and waterborne diseases and changes
in the prevalence of diseases associated with air pollutants and aeroallergens (Mc Carthy et
al, 2001).
Vulnerability is defined by the IPCC as “the degree, to which a system is
susceptible to, or unable to cope with, adverse effects of climate change, including
climate variability and extremes. Vulnerability is a function of the character, magnitude,
and rate of climate change and variation to which a system is exposed, its sensitivity, and its
adaptive capacity‖ (McCarthy et al., 2001).
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This project subscribes to the following notion: the greater the exposure or
sensitivity to the effects of climate change, the greater the vulnerability; the greater
the adaptive capacity, the lower the vulnerability. An assessment of vulnerability must
consider all these components to be comprehensive. An impact of climate change is
typically the effect of climate change. Biophysical systems can undergo change in terms
of productivity, quality, or affect population (in specific numbers or ranges). For societal
systems, impact can be measured as change in value (e.g., gain or loss of income) or in
morbidity, mortality, or other measure of well-being (Parry and Carter, 1998).
The vulnerability of human health to climate change is a function of three
components namely; sensitivity, exposure, and adaptation:
Sensitivity, which includes the extent to which health or nature or social systems on
which health outcomes depend, are sensitive to changes in weather and climate
(exposure–response relationship). Sensitivity to climate change may also be
determined by the characteristics of the population, such as its level of development
and demographic structure;
Exposure to weather or climate-related hazard, including the character, magnitude
and rate of climate variation; and
Adaptation measures and actions in place to reduce the burden of a specific adverse
health outcome (the adaptation baseline). The effectiveness of adaptation measures
are determined in part by the exposure–response relationship.
Populations, subgroups and systems that cannot or will not adapt are more
vulnerable, and may be similar to those that are more susceptible to weather and climate
changes. Understanding a population‘s capacity to adapt to new climate conditions is crucial
to realistically assessing the potential health effects of climate change. In general, the
vulnerability of a population to a health risk depends on factors such as population
density, level of economic development, food availability, income level and
distribution, local environmental conditions, health status, and the quality and
availability of health care. These factors are not uniformly distributed across a region or
country or across time, and differ based on geography, demography and socio-economic
factors. Effectively targeting prevention or adaptation strategies requires
understanding which demographic or geographical subpopulations may be most at
risk and when that risk is likely to increase. Thus, individual, community and
geographical factors determine vulnerability.
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The cause-and-effect chain from climate change due to changing disease patterns
can be extremely complex and includes many non-climatic factors, such as distribution of
income, provision of medical care, and access to adequate nutrition, clean water and
sanitation. Therefore, the severity of impacts actually experienced will be determined not
only by changes in climate but also by concurrent changes in non-climatic factors and by the
adaptation measures implemented to reduce negative impacts.
B. Review of Literature
The review of related literature started from reviewing previous documents that
chronicled the results of previous Climate Change studies in the Philippines. The most
prominent of these was the second Communication Report, which was used as the
springboard of this current project. Few other reports on Climate Change and Health were
similarly perused to situate the project in the process of crafting the vulnerability and
assessment framework.
The Challenge of Climate Change UN Secretary-General Ban Ki-Moon and UNEP Executive Director Achim Steiner
described climate change as ―the defining challenge of our generation.‖ Climate change is a
change in the statistical distribution of weather over periods of time that range from decades
to millions of years. It can be a change in the average weather or a change in the distribution
of weather events around an average (for example, greater or fewer extreme weather
events). Climate change may be limited to a specific region, or may occur across the
whole Earth. It can be caused by recurring, often cyclical climate patterns such as El Niño-
Southern Oscillation (ENSO), or come in the form of more singular events such as the Dust
Bowl (NOAA, 2007).
The climate is changing because of the way people live these days, especially in
richer, economically developed countries. The power plants that generate energy to provide
us with electricity and to heat our homes, the cars and planes that we travel in, the factories
that produce the goods we buy, the farms that grow our food – all these play a part in
changing the climate by giving off what are known as ‗greenhouse gases‘. Climate change
has long-since ceased to be a scientific mystery, and is no longer just a trivial environmental
concern. It is no longer relevant to discuss whether or not our climate is changing, but rather,
how fast changes will occur (European Commission, 2009).
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The Kyoto Protocol is an international agreement linked to the United Nations
Framework Convention on Climate Change. The major feature of the Kyoto Protocol is that it
sets binding targets for 37 industrialized countries and the European community for reducing
greenhouse gas (GHG) emissions .These amount to an average of five per cent against
1990 levels over the five-year period 2008-2012. Under the Treaty, countries must meet
their targets primarily through national measures. However, the Kyoto Protocol offers them
an additional means of meeting their targets by way of three market-based mechanisms,
namely:
Emissions trading – known as ―the carbon market"
Clean Development Mechanism (CDM)
Joint Implementation (JI).
The mechanisms help stimulate green investment and help Parties meet their
emission targets in a cost-effective way. Under the Protocol, countries‘ actual emissions
have to be monitored and precise records have to be kept of the trades carried out.
The Philippines and Climate Change
Climate change is occurring in the Philippines as evidenced by trends of certain
climatic parameters observed by PAGASA for decades. Temperature spikes and warming
have been noted in the northern and southern parts of the country. Moreover, it was noted
that the regions with the highest temperature spikes also experienced drought. Precipitation
trends are largest (10%) during the 20th century (PTFCC, 2007). Hotter days and nights
have been experienced by people. Since the 1980s, extreme weather events occurred more
frequently. These include fatal typhoons, flash floods, landslides, El Nino and La Nina,
drought, and even forest fires. The detrimentally affected sectors which require the most
attention include agriculture, fresh water, coastal and marine resources, and health (PTFCC,
2007).
Typhoon Ondoy devastated Metro Manila as 334 mm of rain flooded the NCR in just
six hours compared to the 1967 typhoon that brought the same area 334 mm of rain in 24
hours (PAGASA spokesman Nathaniel Cruz on TS Ondoy). Continuous media coverage,
post typhoon, where victims of the disaster recounted that they were not prepared for the
disaster, and that there was no warning, noted that most of all help came from the
government and came a bit late. Students were stranded in their schools. Workers became
stranded in their workplaces. Government offices and functions were put to a halt for they
were also stranded. People already in the streets for home or for someplace else were
caught in the dilemma of whether or not brave the rising flood waters and return home or to
just hold their ground, hoping that the waters would eventually subside. The victims, in
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general had one thing in common: everyone was caught off-guard, unprepared and unaware
that such a catastrophe was even possible.
Consequently, outbreaks of various diseases most notably leptospirosis, dengue
fever, and influenza were reported. Aside from being unprepared physically, mentally,
emotionally, financially, and structurally, the people were also unprepared and unaware in
terms of knowledge on the transmission and management of these diseases. As a result,
more lives were lost. Additionally, one of the great needs during the Ondoy and Pepeng
onslaught was the provision of all types of medicines most especially for those who were left
with no choice but to transfer to crowded evacuation centers.
Climate Change Vulnerability and Assessment
Potential health impact of climate variability and change requires understanding of
both the vulnerability of a population and its capacity to respond to new conditions.
Vulnerability is defined as the degree to which individuals and systems are
susceptible to or unable to cope with the adverse effects of climate change, including
climate variability and extremes. Both vulnerability and adaptation need to be understood
to ensure effective risk management of the current and potential effects of climate variability
and change.
The vulnerability of human health to climate change is a function of sensitivity, which
includes the extent to which health, or the natural or social systems on which health
outcomes depend, are sensitive to changes in weather and climate (the exposure–response
relationship) and the characteristics of the population, such as the level of development and
its demographic structure; the exposure to the weather or climate-related hazard, including
the character, magnitude and rate of climate variation; and the adaptation measures and
actions in place to reduce the burden of a specific adverse health outcome (the adaptation
baseline), the effectiveness of which determines in part the exposure–response relationship.
Adaptation includes the strategies, policies and measures undertaken now and in
the future to reduce potential adverse health effects. Adaptive capacity describes the
general ability of institutions, systems and individuals to adjust to potential damages, to take
advantage of opportunities and to cope with consequences. The primary goal of building
adaptive capacity is to reduce future vulnerability to climate variability and change.
Higher temperatures also alter the geographical distribution of species that transmit
disease. For example, outbreaks of dengue and yellow fever, transmitted by mosquitoes,
increase in warmer temperatures.
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Approaches to assessing the potential effects of climate variability and change on
human health vary depending on the outcome of interest. Conventional environmental health
impact assessment is based on the toxicological risk assessment model that addresses
population exposure to environmental agents, such as chemicals in soil, water or air. Most
diseases associated with environmental exposure have many causal factors, which may be
interrelated. These multiple, interrelated causal factors, as well as relevant feedback
mechanisms, need to be addressed in investigating complex associations between disease
and exposure, because they may limit the predictability of the health outcome.
Effects of Climate Change on Health
Climate sensitive diseases include heat-related diseases, water borne diseases,
diseases from urban air pollution, and diseases related to extreme weathers such as flood,
droughts, windstorms and fires. Climate change may also alter the distribution of vector
species. This depends on whether conditions are favorable or unfavorable for them to breed
and continue their reproductive cycle. Temperature can also influence the reproduction and
maturation rate of the infective agent within the vector organism and the survival rate of the
vector organism, thereby further influencing disease transmission.
Changes in climate that can affect the potential transmission of vector-borne
infectious diseases include temperature, humidity, altered rainfall, soil moisture and rising
sea level. Determining how these factors may affect the risk of vector-borne diseases is
complex. The factors responsible for determining the incidence and geographical distribution
of vector-borne diseases are complex and involve many demographic and societal as well
as climatic factors. Transmission requires that the reservoir host, a competent vector and the
pathogen be present in an area at the same time and in adequate numbers to maintain
transmission (Sari Kovats, 2003). Among the vector borne diseases that are climate change
sensitive are dengue, malaria and leptospirosis.
A. Dengue
Dengue is the most common mosquito-borne viral disease of humans that in recent
years has become a major international public health concern. The geographical spread of
both the mosquito vectors and the viruses has led to the global resurgence of epidemic
dengue fever and emergence of dengue hemorrhagic fever (dengue/DHF) in the past 25
years with the development of hyperendemicity in many urban centers of the tropics.
Dengue is a disease caused by any one of four closely related dengue viruses
(DENV 1, DENV 2, DENV 3, or DENV 4). The viruses are transmitted to humans by the bite
12
of an infected mosquito. In the Western Hemisphere, the Aedes aegypti mosquito is the
most important transmitter or vector of dengue viruses, although a 2001 outbreak in Hawaii
was transmitted by Aedes albopictus. It is estimated that there are over 100 million cases of
dengue worldwide each year.
Dengue is transmitted to people by the bite of an Aedes mosquito that is infected with
a dengue virus. The mosquito becomes infected with dengue virus when it bites a person
who has dengue virus in their blood. The person can either have symptoms of dengue fever
or DHF, or they may have no symptoms. After about one week, the mosquito can then
transmit the virus while biting a healthy person. Dengue cannot be spread directly from
person to person.
The principal symptoms of dengue fever are high fever, severe headache, severe
pain behind the eyes, joint pain, muscle and bone pain, rash, and mild bleeding (e.g., nose
or gums bleed, easy bruising). Generally, younger children and those with their first dengue
infection have a milder illness than older children and adults.
There is no specific medication for treatment of a dengue infection. Persons who
think they have dengue should use analgesics (pain relievers) with acetaminophen and
avoid those containing aspirin. They should also rest, drink plenty of fluids, and consult a
physician. If they feel worse (e.g., develop vomiting and severe abdominal pain) in the first
24 hours after the fever declines, they should go immediately to the hospital for evaluation.
Outbreaks of dengue occur primarily in areas where Ae. aegypti (sometimes also Ae.
albopictus) mosquitoes live. This includes most tropical urban areas of the world. Dengue
viruses may be introduced into areas by travelers who become infected while visiting other
areas of the tropics where dengue commonly exists. Aedes aegypti and other mosquitoes
have a complex life-cycle with dramatic changes in shape, function, and habitat. Female
mosquitoes lay their eggs on the inner, wet walls of containers with water. Larvae hatch
when water inundates the eggs as a result of rains or the addition of water by people. In the
following days, the larvae will feed on microorganisms and particulate organic matter,
shedding their skins three times to be able to grow from first to fourth instars. When the larva
has acquired enough energy and size and is in the fourth instar, metamorphosis is triggered,
changing the larva into a pupa. Pupae do not feed; they just change in form until the body of
the adult, flying mosquito is formed. Then, the newly formed adult emerges from the water
after breaking the pupal skin. The entire life cycle lasts 8-10 days at room temperature,
depending on the level of feeding. Thus, there is an aquatic phase (larvae, pupae) and a
terrestrial phase (eggs, adults) in the Aedes Aegypti life-cycle (CDC, 2009).
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B. Malaria
Malaria is a serious and sometimes fatal disease caused by a parasite that
commonly infects a certain type of mosquito which feeds on humans. People who get
malaria are typically very sick with high fevers, shaking chills, and flu-like illness. Four kinds
of malaria parasites can infect humans: Plasmodium falciparum, P. vivax, P. ovale, and P.
malariae. Infection with P. falciparum, if not promptly treated, may lead to death. Although
malaria can be a deadly disease, illness and death from malaria can usually be prevented.
People get malaria by being bitten by an infective female Anopheles mosquito.
Only Anopheles mosquitoes can transmit malaria and they must have been infected through
a previous blood meal taken on an infected person. When a mosquito bites an infected
person, a small amount of blood is taken in which contains microscopic malaria parasites.
About 1 week later, when the mosquito takes its next blood meal, these parasites mix with
the mosquito's saliva and are injected into the person being bitten.
Most cases occur in people who live in countries with malaria transmission. People
from countries with no malaria can become infected when they travel to countries with
malaria or through a blood transfusion (although this is very rare). Also, an infected mother
can transmit malaria to her infant before or during delivery.
Symptoms of malaria include fever and flu-like illness, including shaking chills,
headache, muscle aches, and tiredness. Nausea, vomiting, and diarrhea may also occur.
Malaria may cause anemia and jaundice (yellow coloring of the skin and eyes) because of
the loss of red blood cells. While most people, at the beginning of the disease, have
seemingly benign symptoms like fever, sweats, chills, headaches, malaise, muscles aches,
nausea and vomiting, at the outset, malaria can very rapidly become a severe and life-
threatening disease. Infection with one type of malaria, Plasmodium falciparum, if not
promptly treated, may cause kidney failure, seizures, mental confusion, coma, and death.
For most people, symptoms begin 10 days to 4 weeks after infection, although a
person may feel ill as early as 7 days or as late as 1 year later. Two kinds of malaria, P.
vivaxand P. ovale, can occur again (relapsing malaria). In P. vivax and P. ovale infections,
some parasites can remain dormant in the liver for several months up to about 4 years after
a person is bitten by an infected mosquito. When these parasites come out of hibernation
and begin invading red blood cells ("relapse"), the person will become sick.
Malaria requires two hosts to complete its life cycle. The process starts when a
mosquito, infected with malaria sporozoites, releases them into a human host during
feeding. The sporozoites travel through the bloodstream into the liver, infecting the cells. The
sporozoites mature into schizoints, which explode and release merozoits. The merozoits
latch onto the nearest red blood cell and infect it. Inside the infected red blood cell,
14
trophozoites develop into schizonts and explode, releasing more merozoits or in some
species, develop into male and female gametocytes, where they infect the next mosquito.
These gametocytes reproduce, creating zygotes, which grow into ookenites. The ookenites
attach themselves to the stomach wall of the mosquito, becoming oocysts. These oocysts
grow and explode, releasing sporozoites, which get into the salivary glands of the mosquito.
C. Leptospirosis
Leptospirosis, on the other hand, is a bacterial disease that affects humans and
animals. It is caused by bacteria of the genus Leptospira. In humans it causes a wide range
of symptoms, and some infected persons may have no symptoms at all. Symptoms of
leptospirosis include high fever, severe headache, chills, muscle aches, and vomiting, and
may include jaundice (yellow skin and eyes), red eyes, abdominal pain, diarrhea, or a rash. If
the disease is not treated, the patient could develop kidney damage, meningitis
(inflammation of the membrane around the brain and spinal cord), liver failure, and
respiratory distress. Leptospirosis cases may be fatal.
Outbreaks of leptospirosis are usually caused by exposure to water contaminated
with the urine of infected animals. Many different kinds of animals carry the bacterium; they
may become sick but sometimes have no symptoms. Leptospira organisms have been found
in cattle, pigs, horses, dogs, rodents, and wild animals. Humans become infected through
contact with water, food, or soil containing urine from these infected animals. This may
happen by swallowing contaminated food or water or through skin contact, especially with
mucosal surfaces, such as the eyes or nose, or with broken skin. The disease is not known
to be spread from person to person.
Leptospirosis occurs worldwide but is most common in temperate or tropical
climates. It is an occupational hazard for many people who work outdoors or with animals,
for example, farmers, sewer workers, veterinarians, fish workers, dairy farmers, or military
personnel.
Leptospirosis is treated with antibiotics, such as doxycycline or penicillin, which
should be given early in the course of the disease. Intravenous antibiotics may be required
for persons with more severe symptoms. Persons with symptoms suggestive of leptospirosis
should contact a health care provider. The risk of acquiring leptospirosis can be greatly
reduced by not swimming or wading in water that might be contaminated with animal urine.
Protective clothing or footwear should be worn by those exposed to contaminated water or
soil because of their job or recreational activities.
Cholera and typhoid are water borne diseases usually associated with post disaster
events. The causative organisms are ever present in the environment but cause much harm
15
when sanitary conditions deteriorate and when human immune mechanisms weaken as a
result of disaster exposure. In this project, these two diseases were also considered as
important climate sensitive illnesses that were included in the study.
D. Cholera
Cholera is an acute, diarrheal illness caused by infection of the intestine with the
bacterium Vibrio cholerae. Outbreaks are frequently associated with large populations
affected by disasters and are subjected to poor sanitary conditions such as in evacuation
centers. The infection is often mild or without symptoms, but sometimes it can be severe.
Approximately one in 20 infected persons has severe disease characterized by profuse
watery diarrhea, vomiting, and leg cramps. In these persons, rapid loss of body fluids leads
to dehydration and shock. Without treatment, death can occur within hours.
It is acquired by the ingestion of water or food contaminated with the cholera
bacterium. In an epidemic, the source of the contamination is usually the feces of an infected
person. The disease can spread rapidly in areas with inadequate treatment of sewage and
drinking water.
Cholera can be simply and successfully treated by immediate replacement of the
fluid and salts lost through diarrhea. Patients can be treated with oral rehydration solution, a
prepackaged mixture of sugar and salts to be mixed with water and drunk in large amounts.
This solution is used throughout the world to treat diarrhea. Severe cases also require
intravenous fluid replacement. With prompt rehydration, fewer than 1% of cholera patients
die.
E. Typhoid Fever
Typhoid fever, caused by the pathogen Salmonella typhi, is a systemic disease with
manifestations varying from mild illness with low-grade fever to severe clinical disease with
abdominal discomfort and complications. The disease may be accompanied by onset of
sustained fever, severe headache, malaise, anorexia, relative bradycardia,splenomegaly,
nonproductive cough in the early stage of the illness, and constipation in adults. It is
transmitted through the ingestion of food or beverages contaminated with Salmonella typhi
bacteria from an infected person. The incubation period usually lasts 8-14 days, with a
range of 3-60 days. The severity of the disease depends on the virulence of the strain,
quantity of inoculums ingested, duration of illness before adequate treatment, age and
previous vaccination. A small number of individuals continue to be asymptomatic carriers of
the bacteria even after recovery, hence making them sources of infection.
16
Monitoring and Evaluation of Climate Change
Because of concerns with the growing threat of global climate change from
increasing concentrations of greenhouse gases (GHG) in the atmosphere, more than 176
countries have become Parties to the U.N. Framework Convention on Climate Change
(FCCC) (UNEP/WMO, 1992). The FCCC was entered into force on March 21, 1994, and the
Parties to the FCCC adopted the Kyoto Protocol for continuing the implementation of the
FCCC in December 1997 (UNFCCC, 1997). The Protocol requires developed countries to
reduce their aggregate emissions by at least 5.2% below 1990 levels by 2008 to 2012.
Monitoring and evaluation of climate change mitigation projects is needed to
accurately determine the net GHG, and other, benefits and costs, and to ensure that the
global climate is protected and that country obligations are met. It provides feedback
regarding the implementation of the climate change program or project to managers as well
as to decision makers.
The conduct of monitoring may have a range of objectives, among which include the
need to:
1. Establish a baseline by gathering information on the basic site characteristics prior to
development or to establish current conditions;
2. Establish long term trends;
3. Estimate inherent variation within the environment, which can be compared with the
variation observed in another specific area;
4. Make comparisons between different situations (for example, pre-development and
post development; upstream and downstream; at different distances from a source)
to detect changes; and
5. Make comparisons against a standard or target level.
Some of the suggested criteria in determining indicators for monitoring include the
following:
1. legal, political, economic, ecologic relevance;
2. sensitivity to human activity;
3. measurable
4. predictable
5. appropriate
6. cost-effective
7. sensitive and specific
8. attainable
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Adaptation Practices in Climate Change
Adaptation to climate change is already taking place, but on a limited basis. Societies
have a long record of adapting to the impacts of weather and climate through a range of
practices that include crop diversification, irrigation, water management, disaster risk
management, and insurance. But climate change poses novel risks often outside the range
of experience, such as impacts related to drought, heat waves, and accelerated glacier
retreat and hurricane intensity.
Adaptation measures that also consider climate change are being implemented, on a
limited basis, in both developed and developing countries. These measures are undertaken
by a range of public and private actors through policies, investments in infrastructure and
technologies, and behavioral change. Examples of adaptations to observed changes in
climate include partial drainage of the Tsho Rolpa glacial lake (Nepal); changes in livelihood
strategies in response to permafrost melt by the Inuit in Nunavut (Canada); and increased
use of artificial snow-making by the Alpine ski industry (Europe, Australia and North
America). A limited but growing set of adaptation measures also explicitly considers
scenarios of future climate change. Examples include consideration of sea-level rise in
design of infrastructure such as the Confederation Bridge (Canada) and in coastal zone
management (United States and the Netherlands).
Adaptation measures within societies need to be integrated across sectors.
Health adaptation is dependent on water, forest, agricultural and habitation adaptation
strategies as health effects of climate change will emanate from various sectors. Hence a
broad of adaptation measures were reviewed and analyzed in this study.
C. Limitations of the Health Sector Study
The results of this study are constrained by several factors mostly pertaining to
availability of local data and information linking climate change to health outcomes in the
Philippines. First, the review of literature mostly yielded international information sources on
climate change and health as research on this topic has been sparse. Second, review of
records on selected disease patterns specifically, malaria, dengue, leptospirosis, cholera
and typhoid, revealed that trend data that covers at least ten years or more are not reliable
as there is data for some years while for other years, these are not reliably collected
anymore. Moreover changes in data collection i.e. change from Field Health Information
System (FHIS) to Philippine Integrated Disease Surveillance Reports (PIDSR) have created
some artifacts that affected disease trend analysis especially as we linked this analysis to
meteorological data. The quality of data significantly affected the precision of projection
18
results. Hence much of the disease projections were based on assumptions that the project
team had to utilize.
Third, coordination of agencies in producing data needed for this project was quite
challenging. While NEDA had initially assured the project team that arrangements have
already been made with DOH and PAG-ASA regarding the need to make data available for
this study, the project team had to devise special arrangements and rely on past friendships
and contact to derive essential data for the project.
Lastly, due to time and resource constraints, only three validation sites were chosen
to test the derived health sector V&A Framework. While these study sites were carefully
chosen to be representative of climate vulnerable areas in the whole country, generalization
of the study results to the whole country cannot be done. More extensive application of the
health V&A tools and models need to be accomplished.
D. Project Methods and Validation Sites
The health sector project team utilized a descriptive-analytic design in the conduct of
this study. Current climate change vulnerability of three selected project sites were assessed
using the evolving vulnerability and adaptation framework of the project. Then, adaptation
and mitigation practices observed in three selected project validation sites namely
Pangasinan, Palawan and Rizal provinces were identified and described. These practices
were later analyzed to determine their feasibility and effectiveness to adapt to the changing
climate and mitigate climate change selected sensitive health problems in these areas.
Furthermore, these practices were compared to what are already known effective adaptation
strategies so that they may be integrated into adaptation strategy recommendations that
would be later offered to local governments.
An extensive review of literature was accomplished to situate the project in the
beginning. In addition, the literature review revealed what is already known in the science of
climate change and its impact on health including examples of effective vulnerability
assessment and adaptation planning in many countries around the world. Results of this
review also were used in the documentation of best practices in health sector adaptation
strategies to cope with climate change. These were later annotated to be put together to
compose the compendium of climate change best practices in the health sector.
Data collection consisted of gathering meteorological data from national and local
PAGASA units as well as getting information on target diseases also from national and local
health units. Epidemiologic information describing the distribution of diseases by population
affected and location were also derived. Hence data collection was mostly achieved through
19
the analysis of secondary data that were routinely collected by PAGASA, DOH and
provincial and municipal health units. These data were vital for the disease and alert
threshold modeling as well as for projections of disease prevalence and economic burden
based on PAGASA climate projections for 2020 and 2050. Secondary data from previous
studies were utilized to project climate change impacts on health as well as their costs.
Hence, most assumptions were robust as they were grounded by previous study results.
The validation sites were selected to cover a wide variance of geographic and
development factors namely topography, population density and development. In
Pangasinan, Alaminos and Bolinao were selected to represent coastal rural areas with
recorded cases of Typhoid and Cholera after being hit by typhoons. In Palawan, the
municipality of Brooke‘s Point was chosen to represent coastal, rural areas with a
persistently high prevalence of malaria while Quezon is a typical upland, rural area also with
high malaria prevalence. Rural areas typically have lower population densities compared to
urban areas. The municipalities of Tanay and Teresa in Rizal typified upland areas with high
population densities in urbanization transition. They are referred to as ―rurban‖ in this report.
These areas have high prevalence of dengue cases almost all year round and also reported
cases of Leptospirosis after typhoon Ondoy.
The health sector project team conducted two national consultation round table
discussions (RTDs). The first RTD aimed to present project approaches and the initial
vulnerability and adaptation framework crafted from the review of literature. That consultation
elicited comments and suggestions to improve the framework and fine tune the study
approaches. The second RTD was utilized to elicit comments on preliminary project results
and to determine their acceptability to project key stakeholders. At this time, the vulnerability
and assessment framework for the health sector was finalized. Moreover, the various best
practice adaptation strategies that were documented were presented and discussed.
Validation of the vulnerability and adaptation (V&A) framework was done in the three
aforementioned validation sites through site visits. Meetings with the provincial and
municipal health teams were conducted. The V&A framework was presented to the
municipal health team, the municipal executives and planners as well as to the provincial
health team and provincial health executives and planners. Comments and
recommendations to improve the framework were elicited. Discussions to determine the
implementability of the framework and to elicit best practices from the validation sites were
likewise conducted. Discussions were sometimes preceded by key informant interviews with
key stakeholders like the chief meteorologic officer of satellite PAGASA offices, municipal
health officers, provincial health staff and in Rizal a longtime sanitary inspector (See Book 2,
Appendix J for list of respondents.)
20
After extensive consultation with key project stakeholders, the final report was drafted
and prepared for presentation. Several presentations have been made to the Climate
change commission as well as to the LGU 3i summits held in Albay, Iloilo and Davao.
E. Climate scenarios from PAG-ASA Climate data from PAG-ASA was made available to include temperature and rainfall
measurements for 2010, 2020 and 2050 (See Book 2, Appendix I). These climate scenarios
were supposed to provide information so that predicted epidemiologic and socio-economic
impact of climate change could be described.
These data show that in 2020 and 2050, the minimum and maximum temperatures
will be higher and rainfall will increase. This is attributed to the earth that is warming due to
emissions of greenhouse gases caused by human activity. Current trends in energy use,
unsustainable development and population growth are already held responsible for
continuing – and more severe – climate change. The changing climate will inevitably affect
the basic requirements for maintaining health: clean air and water, sufficient food and
adequate shelter.
Climate change also brings new challenges to the control of infectious diseases.
Many of the major killers are highly climate sensitive as regards temperature and rainfall,
including cholera and the diarrheal diseases, as well as infectious diseases including
malaria, dengue, leptospirosis and others spread by vectors. Results of the study show the
forecasted prevalence of selected diseases based on models that were constructed based
on available data.
Ultimately, climate change threatens to slow, halt or reverses the progress that
the global public health community is now making against many of the
aforementioned diseases. With effective health sector adaptation strategies, the spread of
these targeted health problems may be effectively curbed and ultimately contribute to the
attainment of MDGs directly attributable to health.
21
III. RESULTS OF THE STUDY
A. Vulnerability, Adaptability and Impact Assessment Tools
The project developed tools namely: 1) Vulnerability and Adaptation (V&A)
framework; or adopted appropriate tools such as 2) the burden of disease, 3) Breteau Index
and 4) Vulnerability Maps, 5) climate change, health and socio-economic impact models and
6) the ecologic epidemiologic model to determine the impact of predicted climactic changes
on selected diseases. The project also devised a 6) Adaptation Assessment Tool to evaluate
possible climate change adaptation options. A variety of methods and tools are available to
assess climate change vulnerability and adaptation in the health sector. Both quantitative
and qualitative approaches may be used to assess potential health impacts of climate
change. The three key issues to be addressed are: (1) estimating the current distribution
and burden of climate-sensitive diseases; (2) estimating the future health impacts
attributable to climate change including their costs to society; and (3) identifying current and
future adaptation options to reduce the burden of disease. These tools and their outputs as
a result of their application to the validation sites are discussed in the following section.
Vulnerability and Adaptation Framework
The schematic diagram of the vulnerability and adaptation framework is shown in
Figure 2. The inputs to vulnerability assessment are baseline maps, climate change data,
ecological factors, environmental factors, individual/family/community characteristics, health
system and infrastructure, pathogen/vector factors, socio-economic factors, institutional
factors, and health/environmental policies. The integration of all these factors defines the
vulnerabilities of the people to climate change-related diseases. Highly vulnerable
populations such as infants, children and mothers as well as people with compromised
immunities will most likely be affected. Extreme rainfall events that will lead to flooding and
landslide may cause or further aggravate incidence of diseases. Institutional capabilities for
early warning systems and weather predictions can either mitigate or exacerbate climate
change vulnerabilities of populations.
Vulnerabilities are grouped according to several categories. The first category
identifies who are vulnerable whether they are individuals, families or communities. The
ability to precisely identify who are vulnerable and where they are improves the capability of
society to protect them through specific adaptation measures. Secondly, there are health
system and infrastructure vulnerabilities that will determine how well people can access
22
health services from health promotion, to disease prevention, treatment and rehabilitation.
This also involves determining the sustainability of appropriate health care measures to
maintain human health. The third category refers directly to vector and pathogen factors
including their density, virulence, and capability to spread climate sensitive diseases. The
next set of vulnerabilities covers socio-economic and environmental factors separately.
Incomes, educational status, habitat locations and environmental quality will determine
whether vulnerabilities are heightened or mitigated. Finally, health and other policies will
determine whether vulnerabilities are reduced by effective and efficient interventions in the
public or institutional policy arenas.
The following vulnerabilities were identified by respondents in the field site visits as
well as from the two RTDs conducted:
Potential Vulnerabilities In Relation To Climate Change in the Philippine Health
Sector
1. Individual /Family /Community
Extreme age (e.g., very young and old segment of the population)
Individual susceptibility (e.g., immune system, genetic predisposition, pre-existing diseases)
Presence of indigenous population/communities
Access to safe water supply
Access to sanitation facilities
Access to healthcare / health insurance
Health related behavior (hygiene practices)
Occupational factors
Type of dwelling
Location of residential areas (upland, coastal, rural, urban)
Ability to respond promptly to urgent and long-term impacts of Climate Change
2. Health System and Infrastructure
Accessibility
Facilities and human resource capabilities
Laboratory facilities
Ability to respond to emergency and disasters
Emergency preparedness plan / Disaster Management Plan
Monitoring and disease surveillance capacity
Referral system and networking with other healthcare facilities
Database system
Forecasting and risk assessment capabilities re health impacts of climate change
Risk communication capabilities
23
Level of local and national support
3. Pathogen /Vector Factors
Pathogen/Vector reproduction
Presence of breeding sites
Location of breeding sites vis a vis community sites
Preventive technology (e.g. vaccines, vector control, etc.)
4. Socio – Economic Factors
Income level
Education level
Access to healthcare facilities
Coverage of health insurance (may also be considered as individual/ family factor)
Allocation of resources (local and national) to address vulnerability to CC (e.g. health system and health infrastructure)
Social safety nets (e.g. in Albay public schools are redesigned to be evacuation center-ready facilites)
5. Environmental Factors
Domestic wastewater management practices
Solid waste management practices
Level of water pollution
Level of soil and land pollution
Population density
Susceptibility to flooding and landslides
Degradation of watershed areas
Environmental sanitation conditions
Human settlement conditions and locations
Implementation of zoning and land-use policies
6. Health/Environmental Policy
Adequacy
Relevance of existing health and environmental policy
Level of implementation of environmental health programs (provide incentive system for good implementation)
Applications of adaptation options to reduce disease impacts will reduce the
vulnerability of the affected population thus rendering them disease-resilient. Adaptation
options need to be designed to precisely address specific vulnerability categories. If
successful, climate change impact on the identified target climate-sensitive diseases can be
significantly reduced. Hence, climate change vulnerability will likewise be reduced.
24
Figure 2 Health Sector Climate Change Vulnerability and Adaptation Framework
Burden of Disease
Disability Adjusted Life Year or DALYs is a common metric of BOD study. It is used
to aggregate and summarize the data and information on mortality and non-fatal health
outcomes and thus permit comparisons of the impact of various health-related conditions. As
DALY can also be used in cost-effectiveness analyses, they greatly facilitate the joint
assessment of the magnitude of health problems with the array of interventions-and their
costs-that are available to address these problems. Measures from DALYs provide a way to
compare the magnitude of fatal and non-fatal health problems. (Murray JL and Lopez AD,
1996)
The Global Burden of Disease Study in 1992 commissioned by the World Bank and
the World Health Organization was basically conducted to provide an objective comparable
25
assessment of health status, based on what was then known about the occurrence of
disease and injury throughout the world. The study was the first implementation of the
burden of disease concept at the global and regional levels. It demonstrated that BOD
approach is feasible. The large numbers of National Burden of Disease studies that have
followed also indicate the demand for such comprehensive views of health problems.
(Murray JL and Lopez AD, 1996)
This research project adopted the methodology of the Global Burden of Disease
project to attain a more objective result and to ensure that the results of the study are
comparable with other studies using the same methodology.
The Burden of disease concept as discussed by Murray and Lopez addresses the
usual limitations of available information on magnitude of health problems. Health
information is usually provided to decision makers by disease advocates wherein this link
between advocacy and epidemiological estimation can raise doubts about the objectivity of
the conclusions. In particular, advocates, often unintentionally, tend to overestimate the
magnitude of their specific health concerns. International datasets which are based on
similar diagnostic and reporting procedures fail to incorporate comparable information on
nonfatal health outcomes as they are almost exclusively focused on mortality. With the
absence of plausible information on disability and other indicators of morbidity which
contributed to the neglect of these problems in national and international health policy
debate, the team considered adopting the burden of disease concept in this project.
In the past, most of the assessments of relative importance of different diseases are
based on how many deaths they cause. This method has its merits as death is a clear event
and most countries routinely records the data required in their vital statistical systems.
However, closer scrutiny show that mortality analysis has loopholes because there are no
consistent estimates of adult mortality in many developing countries and the available
mortality estimates are generally confined to infancy and childhood. There are also many
non-fatal conditions which are responsible for great loss of ‗healthy life‘. Disability has not
been included in estimating the burden as it is considered a problem only in societies that
have undergone epidemiological transition (World Development Report, 1993).
With the expanding role of cost-effectiveness in health care planning, the need for
more comprehensive measurement of burden of disease has become more urgent. Thus,
there is an urgent need for a process through which every disease or health problem would
be evaluated objectively so that health programs which do not have strong and able
advocates will not be ignored.
26
Christopher Murray et al have developed a new approach to quantify the burden of
disease which was used by WHO and World Bank to estimate the Global Burden of Disease
(GBD). This indicator, the Disability Adjusted Life Year (DALY) extends the concept of
premature death (Potential years of life lost PYLL) to include equivalent years of ―healthy life
lost because of illness and disability (years lived with disability YLD) (Schopper D, et al,
2000).
Four concepts were proposed by Murray et al in developing a burden of disease
index. The first one is that any health outcome that represents a loss of welfare should
be included in an indicator of health status. If a society is willing to devote resources,
money, manpower or other resources in the prevention, cure or rehabilitation of a certain
illness or outcome, then that health outcome must be included in the total estimated burden.
There is no use considering health outcomes which society is not willing to pay for.
Secondly, the characteristics of individuals affected by a certain outcome to be
included in the associated burden of disease should be restricted to sex and age.
This concept primarily addresses the issue of what is common to all individuals and
communities. The third concept which is treating like outcomes as like, a BOD index
should be able to achieve comparability across different communities and within
communities. Treating like as like means that the premature death of an individual in a rich
country or poor area would mean the same anywhere else in the world, thus, giving an
egalitarian flavor to the index.
The fourth concept proposed by Murray et al is that time is the unit for the burden
of disease. Making use of time as a unit of measure would solve the problem of combining
both morbidity and mortality into one summary index. It is also a more politically-correct
choice of unit of measure than money, when dealing with public health.
The DALY as a measure of the burden of disease was developed with the
aforementioned concepts in mind. While the calculation of the DALY is based on the
standard expected years of life lost (YLL) on model life tables, the value of time lived at
different ages is captured in calculating the DALYs using an exponential function which
reflects the dependence of the young and elderly on adults. The time lived with disability is
made comparable with the time lost due to premature mortality. For this, six classes of
severity of disability have been defined and each class was assigned a disability weight
between 0 to 1. Considering that the DALY measures the future loss, a social discount rate
of three percent has been applied. Details of assumptions used in DALY estimation were
summarized in Global comparative assessments in the health sector edited by CJL Murray
and AD Lopez (1996).
27
Existing Indices used as Basis for Formulating DALYs
Four different methods of estimating time lost due to premature death were explored by
Murray et al:
1. Potential years of life lost (PYLL) – calculated by subtracting the age at time of death from
a potential life expectancy. The products of each death are then added in order to come-up
with the PYLL for the population. This would have been a good basis for the DALY formula,
however, the major disadvantage of this method is shown when dealing with the older age
groups especially so if the life expectancy at birth for that particular country is low. It would
appear that deaths that are over this potential life expectancy do not contribute to the overall
burden.
2. Period expected years of life lost – Instead of using an arbitrary potential limit to life, this
method uses the local period life expectancy at each stage. In this system, all deaths
contribute to the estimated burden. However, since different communities may have
different local period life expectancies at each age thus varying reference standards, and
since life expectancies change over a period of time, comparability among communities and
within communities over time may be problematic.
3. Cohort expected years of life lost- this is similar to the period expected years of life lost
except that the estimated cohort life expectancies are used instead of the local period life
expectancy. Similar problems as in ‗period expected years of life lost‘ are also encountered
in using this method. Cohorts in different communities will also have different life
expectancies, thus the reference standards will also vary.
4. Standard expected years of life lost – in this method, an ideal standard for life expectancy
is adapted. As in the two previous measures, deaths from all ages, in this method contribute
to the estimated total burden of the disease. With the use of an ideal standard,
comparability among communities becomes possible. The standard age used in this method
is the Japanese women‘s life expectancy of 82.5 years which is the highest in the world.
The use of the highest attained life expectancy is also an effective way of promoting the
egalitarian notion since everybody is given equal weights.
The standard years of life lost was used as the basis for the formulation of the DALY
because it ensures comparison among communities, takes into account all age groups and
treats all communities quite fairly.
28
In addition to the basic formula discussed above, several components have been
added to the DALY. Such components have triggered so much discussion when the DALYs
concept was espoused, either because of the very idea of the component itself or the
manner by which such components were measured. These are disability weighting,
discounting, and age weighting.
Disability-Adjusted Life Years
In 1993, the World Bank developed the Disability-Adjusted Life Years (DALY) as a
principal summary measure of population health (Murray CJL, 1996). The reason behind this
was that mortality data becomes less sensitive indicators of change when deaths become
rare (Schopper D, et al, 2000). Also mortality data do not adequately represent a
population‘s health status as they disregard widely prevalent, severely disabling but non-fatal
diseases. The DALY is being groomed as an objective measure that can be used in priority
setting (Mathers CD, et al, 2000), (Murray CJL, 1996). It combines life years lost due to
premature death with life years lost due to living in a disabled state allowing the burden of
disease to be measured as the gap between the current health status and an ideal situation
where people live to old age free of disease and disability (Lopez AD, 2000). DALY can be
computed using the formula:
YLDYLLDALY
Where:
DALY = Disability-Adjusted Life Years
YLL = Years of Life Lost or amount of time in years lost due to premature
death from a specific disease
YLD = Years Lived with Disability or the period of time someone has to live
suffering from a disability brought about by a specific disease
There are different formulae to compute the Years of Life Lost (YLL) and Years Lived with
Disability (YLD) attributable to a specific disease or condition. These formulae will be
discussed in more detail in subsequent sections.
For computations of Years of Life Lost (YLL), Murray used the standard expected years of
life lost method (SEYLL). This is denoted by the formula:
29
l
xxxedSEYLL
0
*
Where:
SEYLL = standard expected years of life lost method
x= age at death
d= number of deaths in the population at each age
*xe = expectation of life at each age x based on some ideal standard
SEYLLs were used because deaths at all ages contribute to the disease burden. Also,
deaths at the same age in all communities contribute equally to the disease burden thus like
outcomes are treated as like.
Disability-Adjusted Life Years (DALY) is a variant of the Quality-Adjusted Life Years
(QALY) but differs due to its standardized assumptions (Andrews G, Sanderson K and Beard
J, 1998). Discounting is applied because people give higher value to their present health
compared to their future health (Andrews G, Sanderson K and Beard J, 1998). Discounting
is the practice of valuing the same thing in the future as less (or more) valuable if one were
to get it in the present (Murray CJL, 1996). Since discounting health benefits is highly
controversial among policy planners, a low positive rate of 3 percent was used in the DALY
computations (Murray CJL, 1996). This low rate was used to account for uncertainty that
increases with time and reduce the problems of excessive sacrifice. While this rate was
selected arbitrarily, it was deemed acceptable for those who argue for and against
discounting after considering arguments from both sides
Age weighting was also introduced. This is denoted by the general formula:
aCe
Where:
β =parameter of the age weighting function
a = age
C = constant
As most societies would choose to save the young to middle-aged individuals
compared to the very young and the very old, age weighting was applied in the computations
(Andrews G, Sanderson K and Beard J, 1998). Arguments, such as the human capital
30
approach, where net producers are given a bigger role in contributing to the social welfare,
and welfare interdependence gives justification for the use of age weights. The formula
above, which introduces a continuous age weighting function that can be mathematically
manipulated, conforms to the age weighting pattern desired. Only a narrow range of and
C, approximately ranging from 0.03 to 0.05, provides age patterns consistent with the
arguments already presented. A of 0.04 was chosen after consultations with the advisory
board. The fact that choosing was arbitrary was not deemed too much an issue because
sensitivity analysis showed that the results were basically insensitive to the choice of . The
important issue was the presence of non-uniform age weights. Discounting and age
weighting were applied per age group.
Methods for Disability-Adjusted Life Years Studies
The methods utilized in previous burden of disease studies can be subdivided into
two parts: methods for estimating Years of Life Lost (YLL) and methods for estimating Years
Lived with Disability (YLD). As these two components of burden of illness are different from
each other, the methods utilized to collect these data are naturally different from each other.
The following section reviews the methods utilized by studies that estimated burden of
disease.
Methods for Estimating Mortality Data (Years of Life Lost)
Years of Life Lost (YLL) is the component of Disability-Adjusted Life Years (DALY) which
summarizes mortality data for a specific disease or condition. It computes years lost due to
premature death using the standard expected years of life lost method (SEYLL) with the
application of age-weighting and discounting (Murray CJL, 1996). It can be computed using
the following formula:
rLaraLrra
er
KareaLre
r
eKCYLLs
11
1)(1))(()(
)())((
2
Where:
r = discount rate for future years; 3% was used
β =parameter of the age weighting function, where 0.04 was used
K = age-weighting modulation factor, which was equal to 1
C = constant, set at 0.1658 due to β=0.04
31
a = age at death
L = standard expectation of life at age a
YLLs compute years of life lost from a disease as the difference between the current
health status and an ideal situation where people live to old age free of disease and disability
(Lopez AD, 2000). Murray and Lopez used the life expectancy at birth of Japanese women
in the computation of YLL (Murray CJL, 1996). This was done on grounds of equity so that
deaths in rich and poor countries will be valued equally. However, Williams argued that this
should not be the case as the practice inadvertently applies an equity weight greater than
one to each year lost in a country where life expectancy at a given age is less than the
standard (Williams A, 1999). He suggested using actual life expectancies for the country in
the calculations.
The Global Burden of Disease Study started in 1992 with the first results published in
1994 (Murray CJL and Lopez ADa, 1994). In the original version of the DALY, causes of
mortality were classified into three big groups: Group I included communicable, perinatal and
maternal conditions, Group II was non-communicable diseases, and Group III was injuries.
Mortality estimates were arrived at using three methods. In areas where there is good vital
registration, they used the deaths as coded by the registration system according to the ninth
revision of the International Classification of Diseases (ICD9). In China and India where
there is no complete vital registration system, data from sample registration systems were
used after adjusting for underreporting. Model-derived estimates of cause-of-death patterns
based on total age-specific mortality were the second source of estimates. The relationship
between Groups I, II, and III, and total mortality for each age group was examined using
indirect techniques developed by Preston. The third source of estimates was built from
disease experts on regional epidemiology patterns for specific diseases. These specialists
gave assessments of incidence, prevalence, remission and case fatality rates based on
review of available data for each disease. Estimates were then subjected to internal
consistency checks using DISMOD, a competing-risks computer model (Murray CJL and
Lopez ADa, 1996). DISMOD uses general population based parameters and disease specific
inputs to check internal consistency of estimates. In the computation of YLL, life expectancy
of 82.5 for females and 80 for males were used in the computation. Age-weights were
applied with an age-weighting modulation factor of 0.04 and a discount rate of 3% was used
for future years. These methods were basically the same used for the second set of DALY
estimates published in 1996 (Murray CJL and Lopez ADa , 1996). One difference was that
nutritional disorders were properly classified in Group I. Nutritional disorders were classified
as non-communicable diseases in the earlier estimates.
32
Other than the Global Burden of Disease Study, two other studies tried to estimate
burden of disease in their respective countries using DALY. Schopper and others (Schopper
D, et al, 2000) tried to estimate burden of disease in Geneva. They used the national
mortality database to estimate mortality (Schopper D, et al, 2000). Deaths were recoded
according to ICD 9 as Switzerland still uses ICD 8. Life expectancies, age-weights, and
discounting rates for future years used were the same with the GBD. On the other hand,
Mathers and others attempted to estimate DALYs for Australia (Mathers CD, et al, 2001)
(Mathers CD, et al, 2000). They made several revisions for YLL computation to make the
results more applicable for Australia. First, the difference in life expectancies of females from
male is 4.2, greater than 2.5 used in GBD. Another alteration from the GBD was the non-
usage of age-weighting. However, a discount rate of 3% for future years was still used.
Methods for Estimating Disability Data (Years Lived with Disability)
Years Lived with Disability (YLD) is defined as the time lived in health states worse than
perfect health (Murray CJL, 1996). This is weighted by a preference weight for each health
state. This can be computed as follows:
rLaraLr
ra
er
KeaLre
r
eKCDYLDs
1
11))((
)(
)())((2
Where:
r = discount rate for future years; 3% was used
β =parameter of the age weighting function, where 0.04 was used
K = age-weighting modulation factor, which was equal to 1
C = constant, set at 0.1658 due to β=0.04
a = age at onset of disability
L = duration of disability
D = disability weight
Because data on disability is not as readily available as mortality data, methods used
for the estimation of YLD are more complicated. The methods used for the original YLD‘s in
1992 were as follows: First, disability was defined into four domains namely, procreation,
occupation, education, and recreation (Murray CJL, 1996). Then, six classes of disability
were arbitrarily identified based on word definitions related to activities of daily living,
33
instrumental activities of daily living, and the four domains. Probabilities of transition from
disease to major disabling sequelae were estimated using available literature and expert
panels. And finally, a panel of public health practitioners was asked to choose the severity
weight for each of the six classes using a rating scale method. However, important criticisms
against this approach were raised in scientific meetings and country applications, which
prompted the authors to revise some of the methods. In the 1996 estimations, revisions were
already made which included shifting away from defining disability class in terms of the four
domains used earlier and having a more deliberative process of choosing weights for any of
the disabling sequelae using the person trade-off approach (Murray CJL and Lopez ADb,
1996).
Having developed the disability weights, the GBD made it easier for succeeding
studies to estimate disability. Schopper and others indirectly estimated YLD through data
published by Murray (Schopper D, et al, 2000). They used YLD/YLL ratios calculated for
health conditions in Established Market Economies (EME) to estimate YLD in Geneva when
this ratio is <10. When the ratio is >10, primary data on incidence, age of onset, duration,
and degree of disability for EME countries as published in Global Burden of Disease Study
(GBD) were used. Mathers and others, in the Australian Burden of Disease Study used
incidence rates directly available for some conditions directly from disease registration
systems or epidemiologic studies (Mathers CD, et al, 2001) (Mathers CD, et al, 2000) For
conditions when only prevalence was available, they used the computer program DISMOD
to model incidence. For disability weights, they used actual or derived weights from GBD
and Dutch disability weights. They used the Dutch disability weights as much as possible as
it was more applicable for Australia. A major revision was that weights were adjusted for co-
morbidity unlike in the GBD and the Dutch Study.
The GBD group made the computations for DALY easier by providing a worksheet
(attached) that computes for DALYs provided certain data points are entered. These data
points are: (1) the number of incident cases (2) average age at onset of the disability (3)
expected duration of the disability (4) the disability weights (5) number of deaths and (6) life
expectancy at age of death.
Breteau Index for predicting Dengue outbreaks
The Breteau index has been linked with the transmission level of the dengue fever
and can be used as a warning indicator of this disease (Wei-Chun et al, 2008). The index is
measured in terms of the number of containers positive for mosquito larvae per 100 houses
34
inspected. This is generally considered to be the best of the commonly used indices (such
as the House Index or the Container Index), since it combines dwellings and containers and
is more qualitative besides having epidemiological significance
Density
Level
1 2 3 4 5 6 7 8 9
Breteau
Index
1 - 4 5 - 9 10 -
19
20 -
34
35 -
49
50 -
78
78 -
99
100 -
199
>
200
Figure 3 The Breteau Index
As shown in Figure 3 when the Breteau index is above 50 (i.e. density level >6), it is
regarded as highly dangerous in terms of transmission of the disease according to the
definition provided by the WHO; above 20 (i.e. density level >4), it is considered to be
sensitive, meaning that a dengue fever epidemic could break out anytime; under 5 (i.e.
density level <2), this means that the disease will not be transmitted.
The formulae used for this index include:
Stage 1 - Yit =f(TEMit,WETit, RAINit−1,Yit−1)
Stage 2 - NDENit = g Yˆit, POPit
Where:
Yit is the density level in county i at time t,
Yit−1 is the one-period lagged density level in county i,
TEMit is the temperature in Celsius in county i at time t,
WETit is the humidity in county i at time t,
RAINit−1 is the one-period lagged rainfall in county i at time t,
NDENit is the number of people infected with dengue fever in county i at time t,
Yˆit is the estimated density level in county i at time t from equation (1),
POPit is the population in county i at time t.
35
The Breteau index is a very practical tool that can be used either at the barangay,
municipal or provincial levels to reliably predict a dengue or malaria outbreak. The sanitary
inspector or the midwife can be trained to use this index and give weekly reports to the rural
health nurse or doctor. This will be a simple but reliable surveillance tool that will give the
health authorities some time to control an imminent outbreak.
The index can be complemented with policy pronouncements that will compel
adherence to disease prevention behavior i.e. regular decanting and destruction of mosquito
breeding sites. Similar successful policies have been implemented in Malaysia where urban
dengue outbreaks were prevented with strict policy implementation.
Vulnerability Maps to precisely locate vulnerability hotspots
The vulnerability map is a visual representation of vulnerable areas or ―hotspots‖. It is
designed to provide national and local planners with a visual reference for areas that are
more vulnerable to the changes in the environment, including the health sector, brought
about by climate change.
Vulnerability map is part of the impact model that is at the center of vulnerability assessment.
Although the vulnerability map is at the center, not all of the factors or variables identified in
the model can be effectively rendered in the map. These other factors and variables would
only clutter the map and make it less understandable.
In coming up with the vulnerability map, the project team initially decided to use available
mapping software technology (i.e., ArcGIS) as tool to build such. Computer-aided mapping
technologies have been observed by the team as common to all the three (3) provincial
validation sites of the project in terms of mapping capabilities. However, during the course
of gathering the necessary datasets that could be rendered in ArcGIS for the project, a
number of limitations were encountered.
Limitations to the Vulnerability Maps
The following limitations have been encountered by the project team in coming up
with computer-aided vulnerability maps:
Unavailability of information/data - One of the difficulties encountered by the team
in its effort to gather datasets and shapefiles (GIS filetype) was the unavailability of
36
information or data itself. It is not absolutely necessary that the data or information
be in a shapefile or geo-referenced (i.e. longitude and latitude readings). It is
sufficient that the data could be linked to a geographic area. For example, the open
pit dumpsites data that the team was able to gather was not geo-referenced but with
a specific barangay-level address, the team will be able to mark this on the map.
This becomes an important piece of information for planners. However, a number of
vital data sets were dropped from the list because they were either not being
collected or not available for dissemination during the visit. This limits the analysis
that could be done at the province and municipality level when assessing for
vulnerabilities to diseases. Table 1 presents the dataset requirements identified by
the project team.
Most often, the team was able to find a map that shows the data needed in building
the vulnerability map, however, the data is in picture format (*.jpg or *.bmp). These
cannot be rendered or manipulated in the mapping software (ArcGIS) being used by
the team. Such predicament posed serious limitations on the team‘s ability to
electronically render, analyze and present the different data sets in a vulnerability
map.
Table 1 Vulnerability Map Dataset Requirements
Variables/Datasets Agency Involved
Rainfall
Temp (Max, Min, Mean)
Relative Humidity
Number of Weather Stations and Locations
PAGASA-DOST
Prevalence and Incidence of Diseases
Age/Sex-Specific Morbidity/Mortality Data
Address of Health Facilities/Coordinates if available
(Longitude and Latitude of health facilities from
Barangay health units to tertiary hospitals)
DOH (NCDPC, IMS)
DOH – PHAP (Hospital
Licensing)
DOH-IMS (Information
Management Office)
Population Information
Low Density/High Density Population (geographical
locations)
NSO
37
Variables/Datasets Agency Involved
Topographic Maps NAMRIA - DENR
Forest Cover FMB - DENR
Watershed and Water Networks DENR / DA
Disaster Preparedness Index of Provinces,
Municipalities
DND / DILG / PNRC
Human Settlement Information DILG
Geohazard Maps NAMRIA / MGB
Rodent/Mosquito Population Research Institute for Tropical
Medicine (RITM)
Landfill Information DENR - NSWMC
Road Networks DPWH – Central Office or from
Planning Offices of each
Province
Safe Water Access NSO
Data Sources - The datasets and shapefiles that the team has been able to gather
so far has been mostly sourced through official channels. However in order to
facilitate the initial discussion for the project, some information were gathered
through unofficial channels.
There were also instances wherein the needed shapefiles are already available;
however, securing these data is another issue. What is being shared for the project
is just a portion or just the picture format of it.
Incompatibility of existing map formats for use in ArcView - The official maps
from NAMRIA are currently rendered in AUTOCAD. Although they are currently in
the process of converting these files to shapefiles, the file format for ArcGIS, this is a
long and tedious process. It would take the team approximately 50 weeks to convert
the AUTOCAD files for Palawan which is composed of 50 map sheets or files at a
rate of 1 week: 1 map sheet. This is the shortest time estimate given to the team by
the NAMRIA if the AUTOCAD file were ―clean‖ or needed fewer adjustments. But
there is no assurance that the Palawan files are clean.
38
According to NAMRIA, among the three focus areas, Palawan is the least priority
when it comes to conversion because it is not a ―geohazard area‖ unlike Pangasinan
and Rizal where some of the map sheets have been converted to shapefiles. This is
a special concern for the team because, even if shapefiles are available, they are not
complete that would allow for a comprehensive analysis of the province. To
illustrate, Figure 4 shows the flood and landslide prone areas in Pangasinan based
on available NAMRIA maps. It can be seen that two large chunks of Pangasinan
(Dagupan City and Infanta areas) cannot be rendered on the map.
Figure 4 Flood and landslide prone areas in Pangasinan
“Misalignment” of some maps to NAMRIA maps. The team has been able to get
shapefiles but, when rendered on ArcGIS, they are misaligned. This is particularly
true for the provincial and municipality boundary files, which shows the administrative
boundaries of the different municipalities and provinces. As can been seen in Figure
5, the boundary shapefiles are ―misaligned‖ when super-imposed over the NAMRIA-
produced shapefiles, such as the forest cover map. Technically, this could be fixed
by manually adjusting the boundary files. But this will never be fully aligned with the
NAMRIA-based files.
39
Figure 5 Pangasinan Provincial and Municipal boundaries rendered over the forest
cover shapefile
No available digitized barangay boundary maps. The team also has difficulty in
securing digitized barangay maps. Such limitation poses a problem since, ideally,
incidences of the diseases should be mapped out in a specific location at the
barangay level in order to come up with a more evidence-based analysis of its
demographic or environmental characteristics.
Use of mapping software tool only at the national and provincial level. Mapping
software technologies are only available and being used for planning activities at the
national and provincial level. Although there are few cities and municipalities in the
country which are also using mapping software technologies, however, their use is
only limited to purposes such as tax mapping and development planning. And since
there is now a paradigm shift in terms of planning hierarchy from top down into
bottom up, it is important to also equip these local planning offices (i.e. municipalities)
with the necessary tools to guide them and carry their respective mandates in
addressing issues in their own localities.
Alternative Method for the Vulnerability Mapping
Ideally, the vulnerability map being envisioned by the team should be rendered and
analyzed electronically. The use of Geographic Information System (GIS) software such as
ArcGIS allows the team more room for modeling the vulnerability assessment. But given the
40
limitations presented above, the team has decided to shift to a more traditional method of
rendering, analyzing and presenting the vulnerability maps. A more manual methodology
allows the team to integrate data in different formats (shapefiles, pictures, print outs) into a
common platform – acetates or similar materials. This conventional method hopes to also
address the issue of technical readiness in using ArcGIS software of some municipalities in
the study area. Through the use of acetates and overlay of the various variable layers (or
maps) being considered in this study, local planners and decision makers will be able to
participate in the planning process and at the same time appreciate the visual presentation
of the vulnerabilities in their respective jurisdictions.
For instance, local planners could overlay the political boundary map (Acetate 1 in
Figure 6) together with other maps/layers such as built-up areas (Acetate 2), flood prone
areas (Acetate 4), and solid waste disposal (Acetate 5) in order to see the locations that are
vulnerable to, say, leptospirosis. Or they can further analyze why incidences of leptospirosis
are high to some areas by looking at the demographic and environmental conditions in these
areas. One factor could be that these areas are the same areas that are flood prone or
where landfills or dumpsites are located.
41
Acetate 1: Political Boundary Map Acetate 2: Built-up Areas Map
Acetate 3: Climate Map Acetate 4: Flood Prone Areas Map
Acetate 5: Solid Waste Disposal Map Acetate 6: Health Facilities Map Figure 6 Sample Maps of Rizal Province in Acetate platform
42
Figure 7 Vulnerability to Summer Temperature
Figure 7 shows the temperature vulnerability map of Laguna Lake Basin. The provinces
located here are the National Capital Region (Metro Manila), Rizal province, Laguna
province and Cavite province. The provinces are further subdivided into municipalities. The
temperature vulnerability map was drawn by taking spot readings of temperature in different
places and plotting the temperature readings, then interpolated to come up with the
temperature map as shown above. Most of the built-up areas such as those located in NCR
are the hottest places. Those still covered with vegetations such as those located in Rizal,
Laguna and Cavite are a bit cooler and areas between NCR and Rizal which are populated
between the hottest and coolest part of the basin have moderate temperature.
Transforming this to vulnerability map makes the provinces/municipalities that are colored
green with low vulnerability, NCR with high vulnerability and part of Rizal with medium
vulnerability.
Taking into account disease occurrence in the different places in the Laguna Lake Basin,
areas in NCR close to the lake where the temperature is high causing drying down of stream
waters to stagnant level is conducive to dengue growth. This makes such areas highly
vulnerable to dengue. On the other hand, areas in Laguna where stream waters are still
flowing even during the summer season are considered less variable to dengue.
43
Superimposing a map of water systems (sealed and open/unsealed) of the different
municipalities in the basin to the temperature map defines the vulnerability to cholera.
Communities with open or unsealed water systems are highly vulnerable to cholera due to
water contamination.
Figure 8 shows the vulnerability of the basin to high rainfall and flood. The map shows the
areas that were flooded during Typhoon Milenyo. The highly vulnerable areas are those
colored with blue. Most of the areas around the lake have the highest vulnerability while
areas outside are less vulnerable to flood.
Figure 8 Vulnerability to high rainfall and flood
Considering leptospirosis, the flood-prone areas are potential sources of leptospriosis.
These areas therefore are highly vulnerable to leptospriosis. The same areas are also
vulnerable to cholera especially communities where their water systems are not sealed type.
The uplands in the basin are less vulnerable to leptosprisos because such areas are not
reached by flood waters.
44
Mapping and superimposing a health infrastructure map with the flood prone map,
temperature map and other environmental attribute maps will change the vulnerability of the
areas to diseases in the basin.
The importance of vulnerability maps is that it could be used as a tool for planning
investments for infrastructure, resettlement, reforestation, and so on. Moreover, planning
officers could also be guided further on what specific measures and strategies to introduce
to target/address climate change vulnerabilities.
B. Climate Change and Health Impact Modeling
There are two approaches to modeling the health impact of climate change.
The first uses a time series analysis to determine the effect of climate change on
Dengue utilizing data from the NCR. The second method uses a combination of
statistical methods to project future cases of selected diseases as well as alert and
epidemic thresholds.
Climate Change and Health Impact Modeling: Statistical Models
Disease impact modeling is one of the tools that can be used by planners of the
LGU, DOH, and NEDA to enable them to predict scenarios of health situation in the country
under climate change, thus, making the Philippines‘ health sector resilient. The capacity of
the planners to determine future outcome of climate change impact on health would enable
the government to implement effective climate change impact adaptation strategies.
Disease impact models are important tools in predicting the future effects of rainfall,
temperature, population of disease vectors, and changes in the environmental conditions to
the growth and incidence of diseases that impact on human health.
The general aim of developing disease impact models for selected climate change-
related diseases is to craft simple models that planners of the LGU, DOH and NEDA can use
to minimize the risks of having disease outbreaks in the future under a climate change
scenario.
Specifically, the modeling effort sought to:
a. Assess and evaluate the availability and adequacy of disease and climate
change data for modeling purposes in NCR, Palawan, Rizal, and Pangasinan;
45
b. Determine which of the climate change indicators or factors contribute to the
increase or decrease of disease cases.
c. Develop a methodology for disease impact modeling; and
d. Project the impacts of climate change to diseases under different climate
change scenarios in 2020 and 2050.
Disease Impact Modeling
Disease impact modeling is one of the tools that can be used by planners of the
LGU, DOH, and NEDA to enable them to predict scenarios of health situation in the country
under climate change, thus, making the Philippines‘ health sector resilient. The capacity of
the planners to see future outcome of climate change impact on health would enable the
government to implement effective climate change impact adaptation strategies.
The general objective of this effort was to develop disease impact models for
selected climate change-related diseases that can be used by planners of the LGU, DOH
and NEDA to minimize the risks of having diseases in the future under a climate change
scenario.
The specific objectives of disease impact modeling were to:
a. assess and evaluate the availability and adequacy of disease and climate
change data for modeling purposes in NCR, Palawan, Rizal, and Pangasinan;
b. determine which of the climate change indicators or factors contribute to the
increase or decrease of disease cases, alert and epidemic thresholds;
c. develop a methodology for disease impact modeling; and
d. project the impacts of climate change to diseases under different climate
change scenarios in 2020 and 2050.
Conceptual Framework
The methodology used in this study is based on the following conceptual framework
(Figure 9):
46
Figure 9 Conceptual Framework for Disease Impact Modeling
The framework shows six interactive components. These are:
a) Health and climate change assessment and evaluation
True, accurate, and adequate health and climate change data are important in
modeling. It is necessary that both health data and climate change data are observed
and recorded at the same place and at the same time. It is also imperative to use
true data, which refers to validated disease data which have passed through
scientific diagnosis approach. Use of suspected disease occurrence would cause
error in the modeling.
Adequacy refers to the optimum number of observations that could be used in
modeling. As a rule of thumb, use of more observations or data distributed in different
climate change conditions is better, resulting in clear pattern or trend. The optimum
number of observations could be established using proven statistical methods.
b) Modeling tools selection
Depending on resource availability, planners can use any of the two general
modeling – the graphical method and the automated computed-based system.
The graphical method is highly appropriate in rural areas where the economic
condition is not favorable. Its usefulness is significant to households which are at the
frontline of disease impact adaptation. Thus, graphical disease impact models could
be done at the barangay level where households would directly benefit from it.
Conceptual Framework
Modeling Tools Selection
Health & Climate
Change Data Assessment
And Evaluation
Model Development, Processing
and Selection
Model Interpretation,
refinement & Validation
Outcome Prediction &
Interpretation
Early
Warning
47
Computer-based system is appropriate in urban areas that are economically well-off
and could afford sustainability of a computer-based system. This method therefore is
expensive compared to graphical method but it is fast for processing data and model
development.
c) Model development, processing, and selection
Model development refers to forming alternative models. Using graphical method,
this is simply drawing the disease data with climate change data one at a time. Model
processing is more appropriate in the computer system where health and climate
change data are processed together to form a model. Model selection is selecting the
best model from a list of developed alternative models using standard selection
criteria that are mostly statistical in nature.
d) Model interpretation, refinement and validation
After screening the alternative models and selecting the best one, the elements of
the best model needs to be interpreted to establish its strengths through statistical
tests. The relevant statistical tests are F-test, T-test and Chi-square test with
corresponding significance levels. Model refinement refers to further improving the
model by increasing or decreasing the data used in its formation and processing
again resulting in better statistical test results. After selecting or refining the data,
further validation strengthens the stability of the model. Validation requires testing the
model with real health and climate change data and statistically testing its
significance.
e) Outcome prediction and interpretation
Outcome prediction refers to using the best model in predicting disease magnitude or
indicator given predicted climate change data. Outcome interpretation refers to
analyzing the predicted values, formulating policies and actions that need to be
implemented in the field to lessen potential adverse impacts of diseases to people in
the locality. In outcome prediction, there are precautionary measures to bear in mind.
These are the limitations of the model usually defined by the range of conditions
where it was developed, its statistical accuracy, and the planners who are using it.
f) Early warning
The very reason for developing disease impact models is to enable LGU, DOH and
NEDA planners prepare governance systems in addressing disease impacts of
48
climate change to health that would occur in the future. In particular, knowing what
lies ahead in terms of the climate change conditions through the predicted results
using the models serves as an early warning to the general public, allowing them to
plan out how to prepare themselves during a climate change scenario.
Limitations of Disease Impact Modeling
There are several limitations of the modeling process in this study. These are:
a. Non-availability of perfectly matched real time health and CC data
The available health data and CC data were not real time and perfectly matched
data. While the health data were actual cases, the CC data were projected to where
the locations of the diseases were observed. The CC data therefore were not taken
at the place when the disease was diagnosed and recorded.
b. Health data do not reflect start and finish time of diagnoses as well as that of
treatment.
The health data provided were actual disease cases recorded during a given day of
the month. There were no data saying that such particular disease case had the
shortest or longest time (number of days) from diagnosis to cure.
c. No lag time data on disease occurrence.
An ideal recording of disease cases vis-a-vis climate change indicators shows the
real time of disease occurrence starting from the first time of diagnosis where
symptoms were observed brought about by a change in temperature or rainfall or
relative humidity to the time when the disease was cured. This recording was not
available during the conduct of the study. Thus, integrating lag time variable to the
modeling was not possible because of lack of data.
d. Incomplete health data
The data provided by the Provincial Health Office in Palawan, Pangasinan and Rizal
derived from their disease surveillance data were incomplete. Some have disease
cases, some were alert thresholds and some were epidemic thresholds.
49
Modeling Process
Modeling tools
There are two general tools used for impact modeling, the graphical approach and
mathematical or statistical modeling, which can be used depending on the availability of
resources and existence of personnel who can operate the system. These tools require
software and hardware support and information resources such as disease and climate
change data.
Graphical Modeling
This tool is the easiest, the least cost method, and could be easily understood by non-
college graduate. It requires only the following:
a. Graphing paper
b. Ruler calibrated in mm and cm
c. Colored pens
Because of its simplicity to use and being the cheapest among the available tools, it could
easily be performed by local planners at the municipal level whether in the health sector or
the local government units. This is simply done by graphing the dependent parameter with
the independent parameter. The dependent parameter is at the y-axis while the
independent parameter is at the x-axis. This method does not require any computer.
Because it is the cheapest tool, even local planners at the Barangay level could prepare their
disease impact models to guide them in the adaptation of the communities to potential
disease impacts of climate change.
Limitation of the graphical method
This tool is only applicable for two variables, namely the dependent variable (e.g. dengue
cases) and the independent variable (e.g. rainfall). If a disease is affected by two or more
climate change variables, impact modeling through graphical form is not appropriate
especially when simultaneous treatment of the contributing climate change factors and when
proofs of significance are necessary. This is where mathematical or statistical modeling is
more useful.
Procedure of graphical modeling
The steps to follow in using graphical method are shown below,
a. Draw the y-axis. This is the vertical line
b. Draw the x-axis. This is the horizontal line
50
c. Plot the observations given the readings in the y-axis and in the x-axis
d. Connect the outer most points inside the plot –region. This represents the maximum
observed values.
e. Connect the inner most points (close to the x-axis) inside the plot- region. This
represents the minimum observed values.
f. The area bounded by the maximum and the minimum observed values define the
region of potential disease occurrence region.
The process is illustrated in Figure 10.
0
5
10
15
20
25
0.0 50.0 100.0 150.0 200.0 250.0 300.0
DE
NG
UE
CA
SE
S
RAINFALL (MM)
Graph of maximum values
Graph of minimum values
Potential disease region
Graphical Modeling
Figure 10 Graphical Representation of Disease Impacts vs. Rainfall
If there are several climate change factors, the planner may decide whether to place all the
graphs of the diseases and climate change factors in one graphing paper or each in
separate graphing paper for clarity.
Mathematical or statistical modeling tool
This tool is best applicable when there are more than two independent variables and one
dependent variable depending on available hardware and software. This requires the use of
the following hardware support:
a. Personal computer and peripherals
51
b. Software: SPSS or any statistical software that can process linear and curvilinear
regression
c. Weather data such as rainfall, temperature, and relative humidity
d. Environmental parameters (existence of stagnant water bodies, moist-areas, garbage
areas, canals, water containers in households‘ surroundings, disease vector habitat)
For disease and climate change data with negatively or positively sloping linear trend,
EXCEL can be used. Its applications in impact modeling for climate change impact on health
are limited by the following constraints:
a. Useful only for disease observations that have linear relationships with the
independent variable (weather parameters). There are diseases and climate change
data with non-linear relationships;
b. Limited to one dependent (any disease unit) and one independent variable (say,
rainfall or temperature); and
c. It approximates trends; accuracy is dependent on the goodness of fit of the line graph
considering the data or observations.
The structure of the linear regression model is shown below,
Y = a + b1X1 + b2X2 + b3X3 (Equation No. 1)
Where: y is the predicted value of the disease (units that can be used are disease
cases, prevalence, alert threshold and epidemic threshold, etc.)
a – constant defining the slope of the line. This is the initial disease impact
level.
b1 – is a coefficient for independent variable X1 (rainfall),
b2 – is a coefficient for independent variable X2 (temperature),
b3 – is a coefficient for independent variable X3 (relative humidity)
X1 – weather parameter (rainfall)
X2 – weather parameter (any of the minimum, maximum or mean
temperature readings)
X3 – other weather parameters (relative humidity)
The other form of the model is a curvilinear model. Curvilinear models are sometimes
known as exponential or quadratic models. The form of curvilinear models is,
52
Y = a + b1X12 + b2X23 (Equation No. 2)
The power or exponent means that the climate change factor has an exponential
factor.
Definition of Terms
In interpreting modeling results, there are important statistical parameters that the planners
from DOH, NEDA and LGU should be familiar of. These are:
a. Dependent variable – refers to the name of the variable to be predicted or estimated.
In this study, the dependent variables are cases, prevalence, alert level, and
epidemic level of dengue, malaria, leptospirosis, cholera, and typhoid specific to this
study. Other diseases may be included.
b. Independent variables – refer to the predictors of the dependent variables. Examples
of the independent variables are climate change indicators such as rainfall,
temperature, relative humidity, and environmental factors. These independent
variables contribute to the variations of the dependent variables.
c. Environmental factors – refer to the environmental conditions that favor the growth of
disease vectors. Examples are the existence of unmanaged waste disposal area
which has turned into a rat habitat, areas with stagnant water where mosquitoes live,
unsanitary water systems in barangays composed of old water delivery system from
source to communities where human waste disposal system infiltrates into water
sources.
d. Climate change indicators/data/parameters – refer to any of the following: rainfall,
temperature (maximum, minimum, mean), and relative humidity.
e. Vector – refers to disease carrier (could be insects, animals, and human beings). It
has been reported by medical practitioners that human urine can be a source of
leptospirosis. Humans or pets infected with malaria or dengue are considered
vectors.
f. Correlation value – indicates the relationship of one independent variable to the
dependent variable. The higher the correlation value, the better. This means that the
correlation between the independent variables and the dependent variable are
stronger. A correlation value higher than 0.5 is favorable. The independent variable
53
Correlations
1 -.371 -.336 -.274 .731**
.052 .081 .176 .000
28 28 28 26 28
-.371 1 .981** .877** -.067
.052 .000 .000 .699
28 36 36 34 36
-.336 .981** 1 .956** .035
.081 .000 .000 .841
28 36 36 34 36
-.274 .877** .956** 1 .134
.176 .000 .000 .451
26 34 34 34 34
.731** -.067 .035 .134 1
.000 .699 .841 .451
28 36 36 34 36
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Alert thresholdD
Max. Temp (°C)D
Mean Temp. (°C)M
Min. Temp. (°C)M
Rainf all (mm)M
Alert
thresholdD
Max. Temp
(°C)D
Mean Temp.
(°C)M
Min. Temp.
(°C)M
Rainf all
(mm)M
Correlation is signif icant at the 0.01 level (2-tailed).**.
therefore should be selected in impact modeling as one or among the predictors.
Using an existing statistical software, the correlation table is shown in Figure 11.
Figure 11 Matrix Showing the Correlations of Independent Variables
g. R2 – is the coefficient of determination defining the goodness of fit of the regression
line to the observed values. The R2 values are given in decimal point which is
converted into percent. R2 values range from 0.1-1.0. An R2 closer to 0.1 has a
weaker goodness of fit and regression models having such R2 values should be
discarded because the models are inferior. Models having 0.5-1.0 R2 values have
stronger goodness of fit and therefore should be selected. Increases in R2 value
indicate increasing acceptability of the model. How should R2 be interpreted? If the
R2 value of a regression model is .800 or 80%, means 80% of the predicted values
are due to the independent variables and 20% are contributed by other variables not
included in the model. This is therefore an error. The R2 can also be interpreted as
the accuracy of the model in predicting values. Using statistical software, the R2 is
part of the summary table shown in the following matrix (Figure 12).
Figure 12 Matrix Showing the R-square Value
Model Summary
.915a .836 .808 17.450
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), prov2, Rainfall (mm)D, prov1,
Max. Temp (°C)D
a.
54
h. F-Test – is used to test the null hypothesis that variation in the independent variables
does not contribute to the variation in the dependent variable. To reinforce the
importance of the F-value, a significance level is also established. As a rule of thumb,
an F-value of greater that 2.0 with a significance level of 1-5% indicates that the null
hypothesis is rejected. This means that any variation in the independent variables
does contribute to the variation of the dependent variable. In this situation, the model
is accepted. On the other hand if the F-value is less than 2.0, the independent
variables do not contribute to the variation in the dependent variable. The model
therefore is rejected. Using statistical software, the F-value is found in the ANOVA
matrix (Figure 13).
Figure 13 ANOVA Matrix
i. T-Test – is used to determine whether the coefficients of the independent variables
and the constant are significant or not. The null hypothesis is b = 0 and the
alternative hypothesis is b # 0. If b = 0 is true then the independent variable having
such coefficient is not significant. Therefore, the independent variable is cancelled
out from the model. If b # 0, then it is significant. Therefore, it should be included in
the model. The t-test and significance levels are indicated in the coefficient table
shown in Figure 14.
ANOVAb
35819.046 4 8954.762 29.407 .000a
7003.811 23 304.514
42822.857 27
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), prov 2, Rainf all (mm)D, prov 1, Max. Temp (°C)Da.
Dependent Variable: Alert thresholdDb.
55
Figure 14 Coefficients Matrix
j. Significance level – The F-value and T-values are accompanied with significance
values which determine the level of error of the F-value and T-value. An example is if
the significance level is 5%, this means that the chance of getting an error is 1 out of
20 or 5 out of 100. A 1% significance level is 1 out of 100 chance of getting an error.
Significance levels from 1 to 5% are highly desirable in regression modeling. The
significance level of the F-value is shown in the ANOVA table while that of the
coefficients are shown in the coefficient table.
k. Chi-square test – refers to the statistical test used in the determination of the
appropriateness or goodness of fit of actual values to the predicted values of a
regression model using actual data. The null hypothesis is X2 =,< x2c(k-1)
(where: c(k-
1) is computed at k-1 degrees of freedom. Reject the null hypothesis, otherwise.
l. Coefficient – refers to the magnitude of growth (coefficient has a positive sign) or
decay (coefficient has a negative sign) contributed by an independent variable to the
dependent variable. In equation no. 1, the b1X1 element means that for every unit of
X1, the predicted value is either reduced (-) or increased (+) by b1. Similarly, in
Equation no. 2, the coefficient b1 of X12 means that for every square of X1, the
predicted value is decreased (-) or increased (+) by b1.
m. Constant – refers to the initial/lowest point of the impact, if the independent variables
are equated to zero.
Coefficientsa
345.078 102.406 3.370 .003
-10.213 3.162 -.774 -3.230 .004
.154 .023 .614 6.733 .000
-34.851 8.586 -.416 -4.059 .000
-48.283 21.028 -.577 -2.296 .031
(Constant)
Max. Temp (°C)D
Rainf all (mm)D
prov1
prov2
Model
1
B Std. Error
Unstandardized
Coeff icients
Beta
Standardized
Coeff icients
t Sig.
Dependent Variable: Alert thresholdDa.
56
1. Statistical Modeling Process: Estimating the current distribution and burden of climate-sensitive diseases
Estimating possible future health impacts of climate change must be based on an
understanding of the current disease burden and recent trends including the incidence and
prevalence of climate-sensitive diseases. In the Philippines, the Department of Health is the
major source of the current burden of climate-sensitive diseases at the national and regional
levels. At the local level, the provincial, city and municipal health offices maintain their
respective health database. These government sources may also provide information on
whether current health services are satisfying demand.
The current associations between climate and disease need to be described in ways
that can be linked with climate change projections. The associations can be based on
routine statistics collected by the Department of Health (e.g., FHSIS, PIDSR) or on published
literature. Adverse health outcomes associated with inter-annual climate variability, such as
El Niños, also could be considered (as we experienced in 1997 and currently during the
summer months of 2010).
a. Disease Impact Modeling and Projected Disease Impacts in 2020 and
2050 and Socioeconomic Impact
Disease impact models for dengue, malaria, cholera were developed out of the available
health data from the National Capital Region (NCR) and from Provincial Health Officers
(PHOs) of the Provinces of Palawan, Pangasinan and Rizal. Climate change (rainfall,
temperature and relative humidity) data used were furnished by PAGASA for the year 1992
to 2009, 2020 and 2050. The disease impact models were used to project disease impacts
in 2020 to 2050. The predictive capacity of the models is highly dependent on the accuracy
of the health and climate change data. Due to insufficiency of data on leptospirosis and
typhoid, no models were developed.
After several runs to screen and select the best models, only 3 models were successfully
developed. The diseases where statistically acceptable models were developed are dengue,
cholera and malaria. The models are:
Dengue Cases = -1267.347 - 0.615 * Monthly Rainfall - 21.389 * Maximum
Temperature + 31.442 * Relative Humidity
57
Cholera Cases = 8.948 + 0.026 * Monthly Rainfall - 1.681 * Maximum Temperature +
0.663 * Relative Humidity
Malaria Cases = -218.918 - 0.089 * Monthly Rainfall + 7.605 * Maximum
Temperature
Both dengue and cholera impact models were found to be sensitive to monthly
rainfall, maximum temperature and relative humidity, whereas malaria is sensitive to
monthly rainfall and maximum temperature.
The models can be interpreted in this way: for every unit of the variables, the
corresponding responses of disease cases are summarized in the table below:
Table 2 Model specifications of disease cases as responses to climate change
variables
Disease Cases
One unit each of the variables will increase or decrease disease per thousand cases
Monthly Rainfall
(mm/day)
Maximum Temperature
(degree centigrade)
Relative Humidity
(%)
Increase Decrease Increase Decrease Increase Decrease
Dengue 615 21,389 31,442
Cholera 26 1,681 663
Malaria 89 7,605
The models in graphical forms together with the observed disease cases and climate
change indicators are shown in the following graphs. Figure 15 shows the graphs for
dengue. Both observed values and predicted values are close together indicating accuracy.
58
Figure 15 Dengue Cases, Projected Cases and CC Indicators
The graphs for Malaria is shown in Figure 16. The dark blue color represents the
graph of malaria cases while the light blue color refers to the projected values. There is also
a close coherence between the observed cases and projected values using the model.
Figure 16 Malaria Cases, Projected Values and CC Indicators
Figure 17 shows that graph of cholera cases vs. projected values. Their fits are
almost perfect. This means that the model values are two close to the real observed cases
of cholera.
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J F M AM J J A S O N D
Un
its
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Dengue Cases
Max. Temperature (Celsius)Relative Humidity (%)
Average Daily Rainfall (mm/day)Model
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J F M A M J J A S O N D
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Maximum Temperature (Celsius)Relative Humidity (%)
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Figure 17 Cholera Cases, Projected Values and CC Indicators
No model was developed for leptospirosis. Instead the observed cases were graphed
with the CC indicators. The results are shown in Figure 18. From the curves of leptospirosis
observed cases and rainfall, there seems to be a pattern where increase in rainfall indicates
an increase in the number of cases. Likewise, a reduction in rainfall means a reduction in
leptospirosis cases.
Figure 18 Leptospirosis Cases and CC Indicators
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Relative Humidity (%)
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Average Daily Rainfall (mm/day)
Max. Temperature (Celsius)
Relative Humidity (%)
60
Similarly, in typhoid where no model was developed due to lack of correlations
among the independent and dependent variables, apparently has similar pattern with
maximum temperature and relative humidity. The relationships, however, are weak (Figure
19).
Figure 19 Typhoid Cases and CC Indicators
Assessment and Evaluation of Disease and Climate Change Data
Analysis and evaluation of health and climate change data showed imperfect
matching and inadequacies causing major problems in developing the models. A remedial
measure adopted was to match projected climate change data from PAGASA in NCR and
the three provinces with health data. The health data were also found incomplete in terms of
actual cases. This is the reason why the models for leptospirosis and typhoid were not
developed.
Monthly averages of disease data from 2002-2007, were provided by the PHO in
Palawan, Rizal, and Pangasinan. On the other hand, the weather data were provided by
PAGASA. However, the weather data were not actually recorded at the same place and time
when the diseases were diagnosed and recorded. The NCR disease data was provided last
together with the PAGASA projection of climate change. These were matched with the
disease data to push through with the modeling exercises. In general, not all the diseases
have complete 12-month average data. The availability of disease and weather data is
summarized in Table 3.
0
10
20
30
40
50
60
70
80
90
J F M A M J J A S O N D
Un
its
Months
Typhoid Cases
Average Daily Rainfall (mm/day)
Max. Temperature (Celsius)
Relative Humidity (%)
61
Table 3 Availability of Disease and Weather Data, Palawan, Rizal, Pangasinan, and NCR
Disease Indicators
National Capital Region
Palawan Rizal Pangasinan Rainfall Maximum
Temperature Minimum
Temperature Mean
Temperature
Initial Vector
Population
Dengue Cases 12m
8md 1md 37dd M m m m n
Alert Threshold n
12md 12md 12md m m m m n
Epidemic Threshold
n 12md 12md 12md m m m m n
Malaria Cases 12m
8md 1md 1md m m m m n
Alert Threshold n
n 12md n m m m m n
Epidemic Threshold
n n 12md 2md m m m m n
Cholera Cases 12m
n n 1md m m m M n
Alert Threshold n
n n 5md m m m M n
Epidemic Threshold
n n n 5md m m m M n
Leptospirosis Cases 12m
n n 1md m m m M n
Alert Threshold n
n 3md 4md m m m M n
Epidemic Threshold
n 5md 9md 6md m m m M n
Typhoid Cases 12
n 1md 9md m m m M n
Alert Threshold n
n 5md n m m m M n
Epidemic Threshold
n n 9md 3md m m m M n
Legend:
8md = 8 months data
n = no data
37dd = 37 days data
m = matched data not sure whether taken at the same time with disease observations.
62
The provincial data for NCR, Palawan, Pangasinan and Rizal are shown in Book 2,
Appendix K Climate Change Impact Modeling Data. It is interesting to note that most disease
cases are blank while alert and epidemic thresholds were provided by the PHOs.
The criteria used in evaluating the health and climate change data for use in the disease
impact modeling are:
1. Possible pairing or matching of climate change and health data in the absence of
health records which already incorporated climate change data.
2. Adequacy of observations. Ideally, the number of data to be used should not be less
than 30 observations to show clear trends. Since the data were monthly averages,
the maximum number of data is 12 observation points. This study had no other
recourse but to use the available data.
3. Existence of health and climate change data relationships. Health data and climate
change relationships are distinct in real data so that manufactured data are easily
identifiable due to lack of patterns or trends.
The differences in processing health data coming from the 4 selected areas provided by
DOH and their PHOs indicate the need to standardize health and climate change data
monitoring form required at different levels.
The data that were provided were processed data, i.e., they are monthly averages from
2002 to 2007, without the necessary climate change data. Averaging the monthly disease
data limits the number of observations to be used in the modeling exercise resulting in more
inferior models. Instead of averaging the disease and climate change data, actual individual
observations should be used. In the case of the NCR, the climate change data were
projections by the PAGASA in daily averages by month by year from 1992 to 2009.
Assumptions Used in the Impact Modeling
1. The data from the DOH in Manila for disease data for NCR, PHOs in Palawan, Rizal,
and Pangasinan are the recognized correct and official data on the following
diseases; a) dengue; b) malaria; c) Leptospirosis; d) cholera; and e) typhoid. There
are no other official data except these data. Disease diagnosis procedures were
properly undertaken by DOH or PHOs, thus establishing the correct types of disease.
63
2. The observations on the diseases in the locality were due to existing population of
disease vectors/carriers that were not recorded. Thus, these variables were not
included in the impact models.
3. That the climate change parameters were correct, exact and true conditions at the
locations when the diseases were diagnosed. The climate change data came from
PAGASA, and these were projections from the nearest weather monitoring stations
to or in the province.
4. That the initial diseases‘ levels were equal to the initial recorded disease levels
indicated in the data coming from the PHOs.
5. That the diseases react to changes or variations in the climate change parameters as
manifested in the health data.
Results and Testing of the Impact Models
The impact modeling procedure and corresponding results are presented and discussed
in the following sections.
How is the disease impact equation formulated?
The equation is formed by referring to the coefficients table (Table 4). The first column
shows the list of variables: constant down to prove in this example. The second column
indicates the non-standardized coefficients composed of B or b coefficients and their
corresponding standard errors. The B or coefficient values are either positive or
negative. From these coefficients, the equation is,
Y = Constant + B 1 Max. Temp + B 2 Rainfall + B 3 Prov1 + B 4 Prov2
From the table, Constant = 345.078, B 1 = - 10.214, B 2 = +0.154, B 3 = -34.851, B 4 = -
48.283. Plug in these coefficients in the general equation form above will give the impact
disease model. The signs of the B coefficients also should be reflected in the equation.
64
Table 4 Sample Coefficient Table
Significant Level of the Model
When is the model acceptable? The answer to this question is given by the F-Test. If the F-
Test value is highly significant, the model as a whole is acceptable and therefore can be
used for predicting values.
Goodness of Fit
The goodness of fit is given by the R-square value of the model. If the r-square
value, say is 0.80, this means that the 80% of the predicted values are due to the predictor
variables and that 20% are attributed to errors which are not part of the model.
The outputs of the tests of the models are shown in Book 2, Appendix L Testing
Climate Change and Health Impact models.
Validating the Impact Models
After developing the impact models, there is a need to test how good they are in predicting
values. Observations not used in the model development may be compared to the
expected/predicted values using the model. The statistical test to be used is Chi-square test.
The formula for this is,
X2 = Sum (observed – expected) ^2 / Expected
Where: observed data refers to actual data;
Expected data – refers to predicted data using the model
Coefficientsa
345.078 102.406 3.370 .003
-10.213 3.162 -.774 -3.230 .004
.154 .023 .614 6.733 .000
-34.851 8.586 -.416 -4.059 .000
-48.283 21.028 -.577 -2.296 .031
(Constant)
Max. Temp (°C)D
Rainf all (mm)D
prov1
prov2
Model
1
B Std. Error
Unstandardized
Coeff icients
Beta
Standardized
Coeff icients
t Sig.
Dependent Variable: Alert thresholdDa.
65
The model has a very good ―goodness of fit‖ if the X2 =, < X2 c(k-1). Otherwise, the model has
a weak goodness of fit or the predicted values are different from the observed values.
How to Use the Models
Since modeling is highly dependent on existing real data which is expensive to generate
because of the value and alternative use of money and the cost of monitoring real data, the
models developed in this study may be used by DOH, NEDA and LGU in the following
alternative uses under the same conditions where the data were collected. Models
adjustments for further refinements for better predictive accuracy of the parameters of the
selected diseases are necessary.
a. For outright use in the projection of diseases in any municipality, province or the
whole country. For municipal model, plug in the CC indicators from the municipality.
For provincial model, plug in the CC indicators from the province. And for national
use, sum all the results of the provincial models to make up the projection for the
whole country. Averaging the CC indicators at the national level is not advisable
because there will be some provinces with projections below or above the average.
b. For further study to improve models developed. Useful for other provinces not
covered in this study by using their actual data on diseases on dengue, malaria,
leptospirosis, cholera and typhoid and rainfall, temperature and relative humidity data
and test whether the observed and expected values are statistically similar using Chi-
square test.
c. Using the data of the provinces, refine the models by changing the coefficients. The
independent variables maybe similar but the coefficients and coefficient of
determination are different..
d. Predicting the impact values serving as early warning to policy makers, planners and
community resource leaders and managers.
66
Projecting Diseases Using the Prediction Models
There are precautionary measures that should be known in using the disease impact models
as well as in using the predicted values. There are:
1. Never go beyond the range of the observed independent variables in assuming their
values in the prediction of disease impacts unless there are data showing adaptation
of the disease vectors in new environmental conditions.
2. All negative Y values do not exist in the real world and such values can be
interpreted as there are levels of the climate change parameters that diseases do not
occur.
3. Models are more accurate if used in areas where the dates were collected all at the
same time instead of pairing the health data with the climate change data.
b. A Time Series Analysis of the Effect of Climate Change on the Incidence of
Dengue in the National Capital Region, 1995-2007
As part of the risk estimation step of Activity 1 (Health Risk Assessment (HRA) on
Climate Change Vulnerability and Adaptation), an epidemiologic model was proposed to
describe the relationship of the different weather elements and the incidence of selected
infectious diseases (malaria, dengue, cholera and leptospirosis) based on data from the
National Epidemiology Center of the Department of Health and the Philippine Atmospheric,
Geophysical and Astronomical Services Administration (PAGASA) of the Department of
Science and Technology. A model that could be developed would be helpful in defining any
relationship between climate factors and disease that could help prepare communities
mitigate the effects of increases in infectious diseases.
Making a predictive model is intended to assess the change in the number of cases
of infectious diseases under future climate change conditions. Apart from the results of
correlation/regression studies in selected areas in the Philippines, a time series analysis was
also conducted for the risk estimation of climate change on health in Metro Manila in order
to: 1) to describe the trend of the incidence of selected infectious diseases and outbreaks
and correlate these with changes in weather elements indicative of climate change (such as
monthly temperature, rainfall and relative humidity) in the National Capital Region during the
67
period covered; and, 2) to develop an extrapolation of the estimates of computed dengue
cases based on climate-related factors using a predictive model.
Methodology
A. Sources of data
The study had been confined to using data from the National Capital Region in order
to ensure the completeness and accuracy of data on selected infectious diseases. Regional
data might relatively be delayed and inaccurate compared to data obtained from Metro
Manila which historically has more cases than other parts of the country.
Data on the number of cases of malaria, dengue, cholera and leptospirosis in the
National Capital Region from 1992 to 2007 were obtained from the National Epidemiology
Center of the DOH. Population census data and projection data was obtained from the
website of the National Statistics Office. Data from the 2007 total Philippine population and
total population of the National Capital Region was obtained from the 2007 census of
Population. The total Philippine population data obtained for 2000 to 2006 and 2008 to 2009
was obtained from a medium-term assumption from the National Statistics Office1. Likewise,
the 2000 to 2006 and 2008 to 2009 total population for NCR was also obtained from the
same website. Population data from 1993 to 1999 were obtained from the Philippine Health
Statistics 1960 – 2005 of the DOH. Population data from 1993 to 1996 were obtained by
multiplying the Philippine population by 13% which was the average proportion of the size of
population of the National Capital Region to the total Philippine population.
Computations for incidence for the infectious diseases listed in Table 5 were
obtained from the abovementioned sources of data. Incidence data are reported below per
100,000 population.
Table 5 Incidence of Infectious Diseases, National Capital Region, 1993 to 2007
Infectious Disease Years Covered Total number of Cases
Dengue 1993-2007 64,757
Cholera 1992-2009 2,729
1 http://www.census.gov.ph/data/sectordata/popproj_tab1r.html
68
Typhoid 1998-2009 4,530
Leptospirosis 1998-2009 879
Malaria 1998-2009* 2,558
*Note: w/o 1995
Due to the large variation of monthly data for rainfall during the 14-year period, these
were transformed using the natural logarithm (ln). The transformed data were used in the
presentation of the seasonal patterns of rainfall along with the other weather elements. All
other data were used in their original raw numeric form.
B. Time Series Analysis A series of line graphs were constructed to show the trend of the incidence through
Excel software (Figure 20 to Figure 23). Based on the relatively higher incidence of dengue,
a specific analysis was undertaken to correlate dengue and various weather elements by
constructing a line graph to describe a time series analysis of rainfall, temperature and
relative humidity with the incidence of dengue.
Further time series analyses were undertaken to develop several models to predict
the number of cases of dengue based on climate factors specifically for 1995 to 2007 (that
had the most completed data from the available 1993-2007 dataset obtained from the DOH).
The Autoregressive Integrated Moving Average (ARIMA) model procedure (SPSS/PASW
version 18) was used to determine the contribution of rainfall (transformed to its natural log),
maximum and minimum temperature and relative humidity in estimating the number of
dengue cases for the whole of the National Capital Region and for each city in NCR.
Seasonality (i.e. periodicity of 12 months) and time lags (i.e. climate factors were all
subjected to a biologically plausible time lag of one month in estimating the number of
dengue cases) were both considered in the development of the models.
Results
Based on the following graphs, the incidence for dengue was far greater than the
incidence for malaria, cholera and leptospirosis (which showed a dramatic increase in 2009
due to the leptospirosis epidemic following the September 26 flooding in the aftermath of
69
Typhoon Ondoy). Data for incidence for malaria, cholera and leptospirosis were adjusted
and presented as per 1,000,000 population instead of per 100,000 population, which was
used for dengue.
Figure 20 Malaria Incidence: National Capital Region, 1993-2009
Figure 21 Dengue Incidence: National Capital Region, 1993-2007
Figure 1: Malaria Incidence:
National Capital Region, 1993 - 2009
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Figure 2: Dengue Incidence:
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Figure 22 Cholera Incidence: National Capital Region, 1992-2009
Figure 23 Leptospirosis Incidence: National Capital Region, 1996-2009
Figure 24 is a line graph describing a time series analysis of rainfall, temperature and
relative humidity along with the incidence of dengue. Based from the data obtained from the
National Epidemiology Center of the DOH, the rise in the incidence of dengue cases is
Figure 3: Cholera Incidence:
National Capital Region, 1992 - 2009
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n
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generally from April to October and this observation apparently coincides with increases in
maximum and minimum temperature as well as relative humidity and seasonal increases in
rainfall.
There are six observable instances of concordance between the peaks of dengue
incidence and the abovementioned climate factors or weather elements in Metro Manila from
1993 to 2007: (refer to Book II, Appendix G)
i. July 1996 - Feb 1997
ii. July 1998 - Feb 1999
iii. July 2001 - Oct 2001
iv. Aug 2005 - Dec 2005
v. July 2006 - Feb 2007
vi. July 2007 - past Dec 2007
72
Figure 5: Weather Elements and Dengue Incidence:
National Capital Region, 1993 - 2007
0
10
20
30
40
50
60
70
80
90
100
Jan-93 Apr-93 Jul-93 Oct-93 Jan-94 Apr-94 Jul-94 Oct-94 Jan-95 Apr-95 Jul-95 Oct-95 Jan-96 Apr-96 Jul-96 Oct-96 Jan-97 Apr-97 Jul-97 Oct-97 Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07
Year and Month - by Quarter
Rainfall (ln) Max Temp Min Temp Rel Humidity Dengue Incidence
Figure 24 Weather Elements and Dengue Incidence: National Capital Region, 1993-2007
73
Trend lines (in white) were observed for dengue incidence and for relative
humidity. With a documented El Niño phenomenon in 1998 that manifested as an
increase in both maximum and minimum temperature (for the months of March to
May), a spike in the incidence of dengue was observed a few months later. Similar
spikes were observed in 2006 and 2007 in dengue incidence but this may be partially
explained by a facilitated degree of reporting with the implementation of the
Philippine Integrated Disease Surveillance and Response (PIDSR) system.
Regarding the ARIMA model results, the table below summarizes the data for
each model:
Table 6 ARIMA Model Parameters to Predict the Number of Dengue Cases,
1995-2007
Estimate SE t Sig.
NCR Model NCR Monthly Cases Constant 5521.730 2790.687 1.979 .050
AR, Seasonal Lag 1
.249 .099 2.526 .013
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-41.013 43.665 -.939 .349
Numerator, Seasonal
Lag 1
-.229 1.085 -.211 .833
Max Temp Delay 1
Numerator Lag 0
-114.429 54.775 -2.089 .039
Numerator, Seasonal
Lag 1
-2.136 1.157 -1.846 .067
Min Temp Delay 1
Numerator Lag 0
233.158 58.408 3.992 .000
Numerator, Seasonal
Lag 1
-.655 .301 -2.178 .031
Relative Humidity Delay 1
Numerator Lag 0
2.268 12.732 .178 .859
Numerator, Seasonal
Lag 1
14.349 80.650 .178 .859
Caloocan Model Caloocan Monthly Cases Constant 557.982 319.106 1.749 .083
AR, Seasonal Lag 1
.044 .094 .462 .645
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-4.918 5.635 -.873 .384
Numerator, Seasonal
Lag 1
.529 1.369 .386 .700
Max Temp Delay 1
Numerator Lag 0
-13.360 6.966 -1.918 .057
Numerator, Seasonal
Lag 1
-1.762 1.188 -1.483 .140
Min Temp Delay 1
Numerator Lag 0
26.563 7.431 3.575 .000
Numerator, Seasonal
Lag 1
-.543 .356 -1.528 .129
Relative Humidity Delay 1
74
Estimate SE t Sig.
Numerator Lag 0
.273 1.627 .168 .867
Numerator, Seasonal
Lag 1
13.597 79.927 .170 .865
Las Pinas Model Las Pinas Monthly Cases Constant 97.951 67.585 1.449 .150
AR, Seasonal Lag 1
-.018 .091 -.198 .844
Rainfall (Natural Log) Delay 1
Numerator Lag 0
.753 1.249 .603 .548
Numerator, Seasonal
Lag 1
-2.152 3.949 -.545 .587
Max Temp Delay 1
Numerator Lag 0
-.768 1.538 -.499 .618
Numerator, Seasonal
Lag 1
-3.554 8.097 -.439 .661
Min Temp Delay 1
Numerator Lag 0
.888 1.651 .538 .591
Numerator, Seasonal
Lag 1
-.042 1.964 -.021 .983
Relative Humidity Delay 1
Numerator Lag 0
.273 .360 .758 .450
Numerator, Seasonal
Lag 1
1.375 1.915 .718 .474
Makati Model Makati Monthly Cases Constant 211.865 102.831 2.060 .041
AR, Seasonal Lag 1
.149 .100 1.487 .139
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-2.671 1.737 -1.538 .126
Numerator, Seasonal
Lag 1
-.630 .739 -.852 .396
Max Temp Delay 1
Numerator Lag 0
-5.996 2.095 -2.862 .005
Numerator, Seasonal
Lag 1
-1.432 .673 -2.127 .035
Min Temp Delay 1
Numerator Lag 0
9.033 2.276 3.969 .000
Numerator, Seasonal
Lag 1
-.736 .336 -2.192 .030
Relative Humidity Delay 1
Numerator Lag 0
.049 .497 .098 .922
Numerator, Seasonal
Lag 1
21.804 221.396 .098 .922
Malabon Model Malabon Monthly Cases Constant 72.319 115.916 .624 .534
AR, Seasonal Lag 1
.348 .096 3.611 .000
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-1.093 1.645 -.665 .508
Numerator, Seasonal
Lag 1
-.520 1.548 -.336 .738
Max Temp Delay 1
Numerator Lag 0
.512 2.193 .233 .816
Numerator, Seasonal
Lag 1
16.597 71.820 .231 .818
Min Temp Delay 1
75
Estimate SE t Sig.
Numerator Lag 0
5.372 2.294 2.342 .021
Numerator, Seasonal
Lag 1
-.911 .529 -1.721 .087
Relative Humidity Delay 1
Numerator Lag 0
.697 .493 1.413 .160
Numerator, Seasonal
Lag 1
1.534 1.354 1.133 .259
Mandaluyong Model
Mandaluyong Monthly Cases
Constant 112.230 104.103 1.078 .283
AR, Seasonal Lag 1
.766 .089 8.591 .000
Rainfall (Natural Log) Delay 1
Numerator Lag 0
.026 1.226 .021 .983
Numerator, Seasonal
Lag 1
28.961 1376.071 .021 .983
Max Temp Delay 1
Numerator Lag 0
-1.595 1.697 -.939 .349
Numerator, Seasonal
Lag 1
-4.058 3.935 -1.031 .304
Min Temp Delay 1
Numerator Lag 0
5.413 1.818 2.977 .003
Numerator, Seasonal
Lag 1
-.792 .317 -2.498 .014
Relative Humidity Delay 1
Numerator Lag 0
-.032 .369 -.086 .932
Numerator, Seasonal
Lag 1
-24.192 277.134 -.087 .931
Manila Model Manila Monthly Cases Constant 57.844 660.301 .088 .930
AR, Seasonal Lag 1
.315 .099 3.192 .002
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-.012 9.710 -.001 .999
Numerator, Seasonal
Lag 1
-2.005 1651.096 -.001 .999
Max Temp Delay 1
Numerator Lag 0
-7.976 12.568 -.635 .527
Numerator, Seasonal
Lag 1
-2.671 4.397 -.607 .545
Min Temp Delay 1
Numerator Lag 0
27.890 13.337 2.091 .038
Numerator, Seasonal
Lag 1
-.399 .491 -.813 .418
Relative Humidity Delay 1
Numerator Lag 0
2.367 2.865 .826 .410
Numerator, Seasonal
Lag 1
.630 1.465 .430 .668
Marikina Model Marikina Monthly Cases Constant 112.996 78.724 1.435 .154
AR, Seasonal Lag 1
.402 .098 4.105 .000
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-.975 1.126 -.865 .388
Numerator, Seasonal
Lag 1
-.364 1.151 -.317 .752
Max Temp Delay 1
76
Estimate SE t Sig.
Numerator Lag 0
-.575 1.419 -.406 .686
Numerator, Seasonal
Lag 1
-12.532 30.660 -.409 .683
Min Temp Delay 1
Numerator Lag 0
4.004 1.532 2.614 .010
Numerator, Seasonal
Lag 1
-1.334 .587 -2.272 .025
Relative Humidity Delay 1
Numerator Lag 0
.205 .330 .620 .536
Numerator, Seasonal
Lag 1
5.152 8.665 .595 .553
Muntinlupa Model Muntilupa Monthly Cases Constant 91.160 58.595 1.556 .122
AR, Seasonal Lag 1
.481 .100 4.788 .000
Rainfall (Natural Log) Delay 1
Numerator Lag 0
.540 .812 .665 .507
Numerator, Seasonal
Lag 1
-.257 1.487 -.173 .863
Max Temp Delay 1
Numerator Lag 0
-1.555 1.035 -1.502 .135
Numerator, Seasonal
Lag 1
-2.170 1.478 -1.468 .145
Min Temp Delay 1
Numerator Lag 0
1.988 1.113 1.787 .076
Numerator, Seasonal
Lag 1
-1.224 .783 -1.563 .120
Relative Humidity Delay 1
Numerator Lag 0
.018 .239 .076 .939
Numerator, Seasonal
Lag 1
22.732 300.558 .076 .940
Navotas Model Navotas Monthly Cases Constant 167.517 84.080 1.992 .048
AR, Seasonal Lag 1
.017 .095 .175 .861
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-2.533 1.483 -1.708 .090
Numerator, Seasonal
Lag 1
.033 .609 .055 .956
Max Temp Delay 1
Numerator Lag 0
-5.583 1.890 -2.954 .004
Numerator, Seasonal
Lag 1
-.807 .492 -1.641 .103
Min Temp Delay 1
Numerator Lag 0
8.639 1.992 4.337 .000
Numerator, Seasonal
Lag 1
-.212 .252 -.841 .402
Relative Humidity Delay 1
Numerator Lag 0
.039 .437 .089 .929
Numerator, Seasonal
Lag 1
21.625 240.120 .090 .928
Paranaque Model Paranaque Monthly Cases Constant 196.792 160.804 1.224 .223
AR, Seasonal Lag 1
.228 .122 1.868 .064
Rainfall (Natural Log) Delay 1
77
Estimate SE t Sig.
Numerator Lag 0
-.047 2.573 -.018 .986
Numerator, Seasonal
Lag 1
10.149 575.565 .018 .986
Max Temp Delay 1
Numerator Lag 0
-4.479 3.175 -1.411 .161
Numerator, Seasonal
Lag 1
-1.793 1.525 -1.176 .242
Min Temp Delay 1
Numerator Lag 0
8.493 3.403 2.496 .014
Numerator, Seasonal
Lag 1
-.359 .435 -.825 .411
Relative Humidity Delay 1
Numerator Lag 0
.361 .743 .486 .628
Numerator, Seasonal
Lag 1
2.678 5.794 .462 .645
Pasay Model Pasay Monthly Cases Constant 199.333 162.328 1.228 .222
AR, Seasonal Lag 1
.466 .145 3.209 .002
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-2.152 2.286 -.942 .348
Numerator, Seasonal
Lag 1
-.284 1.051 -.270 .787
Max Temp Delay 1
Numerator Lag 0
-4.974 2.890 -1.721 .088
Numerator, Seasonal
Lag 1
-1.556 1.004 -1.551 .123
Min Temp Delay 1
Numerator Lag 0
8.046 3.125 2.575 .011
Numerator, Seasonal
Lag 1
-.617 .410 -1.504 .135
Relative Humidity Delay 1
Numerator Lag 0
.287 .672 .427 .670
Numerator, Seasonal
Lag 1
4.053 10.155 .399 .690
Pasig Model Pasig Monthly Cases Constant -58.021 224.831 -.258 .797
AR, Seasonal Lag 1
.262 .130 2.009 .047
Rainfall (Natural Log) Delay 1
Numerator Lag 0
.000 3.559 .000 1.000
Numerator, Seasonal
Lag 1
1.860 179421.425 .000 1.000
Max Temp Delay 1
Numerator Lag 0
-3.341 4.397 -.760 .449
Numerator, Seasonal
Lag 1
-.670 1.662 -.403 .687
Min Temp Delay 1
Numerator Lag 0
8.657 4.707 1.839 .068
Numerator, Seasonal
Lag 1
.011 .550 .019 .985
Relative Humidity Delay 1
Numerator Lag 0
.531 1.022 .520 .604
Numerator, Seasonal
Lag 1
-.462 2.133 -.216 .829
78
Estimate SE t Sig.
Pateros Model Pateros Monthly Cases Constant 27.775 29.120 .954 .342
AR, Seasonal Lag 1
-.022 .102 -.214 .831
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-.491 .516 -.951 .343
Numerator, Seasonal
Lag 1
.062 1.123 .055 .956
Max Temp Delay 1
Numerator Lag 0
-.497 .632 -.787 .433
Numerator, Seasonal
Lag 1
-3.033 4.495 -.675 .501
Min Temp Delay 1
Numerator Lag 0
1.562 .669 2.334 .021
Numerator, Seasonal
Lag 1
-.328 .509 -.644 .520
Relative Humidity Delay 1
Numerator Lag 0
.118 .148 .796 .427
Numerator, Seasonal
Lag 1
1.686 2.188 .770 .443
Quezon City Model Quezon City Monthly Cases Constant 612.032 689.940 .887 .377
AR, Seasonal Lag 1
.515 .084 6.127 .000
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-11.154 9.310 -1.198 .233
Numerator, Seasonal
Lag 1
-.416 .816 -.510 .611
Max Temp Delay 1
Numerator Lag 0
-18.568 12.097 -1.535 .127
Numerator, Seasonal
Lag 1
-2.539 1.622 -1.566 .120
Min Temp Delay 1
Numerator Lag 0
48.993 13.095 3.741 .000
Numerator, Seasonal
Lag 1
-.826 .293 -2.813 .006
Relative Humidity Delay 1
Numerator Lag 0
.287 2.769 .104 .918
Numerator, Seasonal
Lag 1
20.117 196.346 .102 .919
San Juan Model San Juan Monthly Cases Constant -6.380 50.035 -.128 .899
AR, Seasonal Lag 1
.618 .088 7.034 .000
Rainfall (Natural Log) Delay 1
Numerator Lag 0
.000 .648 -.001 .999
Numerator, Seasonal
Lag 1
-2.671 3590.017 -.001 .999
Max Temp Delay 1
Numerator Lag 0
-.344 .855 -.402 .688
Numerator, Seasonal
Lag 1
-1.045 3.028 -.345 .731
Min Temp Delay 1
Numerator Lag 0
1.328 .921 1.441 .152
Numerator, Seasonal
Lag 1
-.238 .641 -.372 .711
79
Estimate SE t Sig.
Relative Humidity Delay 1
Numerator Lag 0
-.032 .193 -.168 .867
Numerator, Seasonal
Lag 1
-.450 5.827 -.077 .939
Taguig Model Taguig Monthly Cases Constant 291.684 158.119 1.845 .067
AR, Seasonal Lag 1
.133 .113 1.174 .242
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-1.252 2.683 -.467 .642
Numerator, Seasonal
Lag 1
-1.315 3.355 -.392 .696
Max Temp Delay 1
Numerator Lag 0
-4.157 3.321 -1.252 .213
Numerator, Seasonal
Lag 1
-3.487 3.075 -1.134 .259
Min Temp Delay 1
Numerator Lag 0
10.481 3.526 2.972 .004
Numerator, Seasonal
Lag 1
-.778 .461 -1.685 .094
Relative Humidity Delay 1
Numerator Lag 0
.491 .771 .637 .525
Numerator, Seasonal
Lag 1
3.469 5.451 .636 .526
Valenzuela Model Valenzuela Monthly Cases Constant 270.178 179.792 1.503 .135
AR, Seasonal Lag 1
-.034 .091 -.371 .711
Rainfall (Natural Log) Delay 1
Numerator Lag 0
-3.536 3.342 -1.058 .292
Numerator, Seasonal
Lag 1
.018 .981 .018 .986
Max Temp Delay 1
Numerator Lag 0
-9.664 4.116 -2.348 .020
Numerator, Seasonal
Lag 1
-1.176 .778 -1.511 .133
Min Temp Delay 1
Numerator Lag 0
18.508 4.411 4.195 .000
Numerator, Seasonal
Lag 1
-.312 .281 -1.112 .268
Relative Humidity Delay 1
Numerator Lag 0
-.560 .966 -.580 .563
Numerator, Seasonal
Lag 1
-1.715 3.810 -.450 .653
80
Table 7 Summary of p-values for Predicting the Number of Dengue Cases
using Climate Factors
Model
Autoregression
(One Month
Lag)
Rainfall (Natural Log) Maximum Temperature Minimum Temperature Relative Humidity
Current
Month
One Month
Lag
Current
Month
One Month
Lag
Current
Month
One Month
Lag
Current
Month
One Month
Lag
NCR Model 0.013 0.349 0.833 0.039 0.067 0.000 0.031 0.859 0.859
Caloocan 0.645 0.384 0.700 0.057 0.140 0.000 0.129 0.867 0.865
Las Pinas 0.844 0.548 0.587 0.618 0.661 0.591 0.983 0.450 0.474
Makati 0.139 0.126 0.396 0.005 0.035 0.000 0.030 0.922 0.922
Malabon 0.000 0.508 0.738 0.816 0.818 0.021 0.087 0.160 0.259
Mandaluyong 0.000 0.983 0.983 0.349 0.304 0.003 0.014 0.932 0.931
Manila 0.002 0.999 0.999 0.527 0.545 0.038 0.418 0.410 0.668
Marikina 0.000 0.388 0.752 0.686 0.683 0.010 0.025 0.536 0.553
Muntinlupa 0.000 0.507 0.863 0.135 0.145 0.076 0.120 0.939 0.940
Navotas 0.861 0.090 0.956 0.004 0.103 0.000 0.402 0.929 0.928
Paranaque 0.064 0.986 0.986 0.161 0.242 0.014 0.411 0.628 0.645
Pasay 0.002 0.348 0.484 0.088 0.123 0.011 0.135 0.670 0.690
Pasig 0.047 1.000 1.000 0.449 0.687 0.068 0.985 0.604 0.829
Pateros 0.831 0.343 0.956 0.433 0.501 0.021 0.520 0.427 0.443
Quezon City 0.000 0.233 0.611 0.127 0.120 0.000 0.006 0.918 0.919
San Juan 0.000 0.999 0.999 0.688 0.731 0.152 0.711 0.867 0.939
Taguig 0.242 0.642 0.696 0.213 0.259 0.004 0.094 0.525 0.526
Valenzuela 0.711 0.292 0.986 0.020 0.133 0.000 0.268 0.563 0.653
The above results in Table 7 provide consistent results that the observed
minimum temperatures for the current month provide the most significant positive
contributions to the model to predict the number of dengue cases for any month of
observation. The significant contribution of observed minimum temperature is seen in
most of the cities in NCR and is summarized in the following table:
Table 8 Number of cases predicted by minimum temperature
Model Predicted Number of Dengue Cases per each 1oC increase
in recorded minimum temperature
NCR 233
Caloocan 27
Makati 9
Malabon 5
Mandaluyong 5
Manila 28
Marikina 4
Navotas 9
Paranaque 9
Pasay 8
Pateros 2
Quezon City 49
Taguig 10
Valenzuela 19
In Book 2, Appendix G is a series of line graphs that present the actual and model-
fitted data for NCR and each of the cities in NCR.
81
Discussion
Outbreaks of dengue are apparently cyclical in nature and are known to
depend on epidemiological as well as immunological factors that influence the
dynamics of disease. However, although both herd immunity and the predominance
of specific dengue virus serotypes in the areas are ideal data to be used for analysis
in forecasting, the lack of local seroprevalence surveys undermine attempts to
assess the effects of these determinants on dengue incidence. Based on monthly
data of the number of dengue cases that was provided by the Department of Health –
National Epidemiology Center, the ARIMA analyses was nonetheless limited to the
years 1995-2007 after reviewing the datasets for completeness. Moreover, data for
weather elements obtained from PAGASA was assumed to fairly apply to all cities in
NCR.
82
Figure 25 Time Series Models of the Numbers of Dengue Cases
83
Based on the results of the predictive models for the number of dengue cases, it is
apparent that recorded minimum temperature from PAGASA can be used to estimate the
number of dengue cases for NCR and for selected cities. However, it should be mentioned
that these models depended heavily on assumptions of the adequacy of the level of
reporting of cases as well as the actual intensity of dengue virus transmission in
these areas. Thus, the robustness of the models could relatively be more appreciated in
areas where intense dengue virus transmission was documented through reliable reporting
each month during the time period covered in the study. This would be helpful in interpreting
the results of Figure 25 (Time Series Models of the Number of Dengue Cases) in
determining which Local Government Unit would have a relatively better fit of the model
based on the adequate disease reporting in areas known to often experience dengue
outbreaks.
Nevertheless, comparing the autoregression (i.e. ARIMA) analysis results with the
patterns of dengue incidence and climate factors in Figure 24 (Weather Elements and
Dengue Incidence: National Capital Region, 1993-2007), it is important to note that peaks of
minimum temperature, observed usually from April to June, seem to be followed by a
dramatic increase in dengue incidence within a month‘s time. A peak of dengue incidence
occurs thereafter in about a month after the start of the increase of cases. This
coincides with the years when a surge of cases was experienced in NCR (as was
mentioned previously: 1996, 1998, 2001, 2005 and 2006). (This is evident as crests of
maximum temperature seem to frequently transpire a little earlier compared to the
peaks of minimum temperature. This would be consistent with the lack of significance
in estimates for dengue cases based on maximum temperature.)
Regarding the generalizability of the results of the models developed, though the
data analysis had solely used data from cities of NCR, it would be potentially useful to also
apply the results to other LGUs elsewhere (i.e. other urban areas) where communities
have experienced outbreaks of dengue in the past. It is therefore critical that local health
officials should work closely with national health authorities to coordinate efforts in mitigating
the effects of a rise in temperature (i.e. recorded minimum temperature) on a possible
increase in dengue cases and/or the occurrence of dengue outbreaks particularly during
periods when an occurrence of an El Niño/La Niña –Southern Oscillation (ENSO) condition
in the Pacific Ocean is announced and experienced.
84
2. Estimating the future potential health impacts attributable to climate change
Once the current burden of disease is described, models of climate change or
qualitative expert judgments on plausible changes in temperature and precipitation over a
particular time period can be used to estimate future impacts. Health models can be
complex spatial models or can be based on a simple relationship between exposure and
response. Models of climate change should include projections of how other relevant factors
could change in the future, such as population growth, income, water and sanitation
coverage, and other relevant factors. Projections from models developed for other sectors
can be incorporated, such as projections for flood risk, changes in food supply and land use
changes.
The exercise of attributing a portion of a disease burden to climate change is in its
early infancy. Analysis should consider both the limits of epidemiologic evidence and the
ability of the model to incorporate the non-climatic factors that also determine a health
outcome. For example, the portion of deaths due to natural climatic disasters that can be
attributed to climate change will reflect the degree to which the events can be related to
climate change. For vector-borne diseases, other factors, such as population growth and
land use, may be more important drivers of disease incidence than climate change.
There are three possible approaches such as: (1) comparative risk assessment; (2)
disease-specific models; and (3) qualitative assessment.
a. Comparative risk assessment
Comparative risk assessment was used as part of the WHO Global Burden of
Disease project to estimate how much disease climate change could cause globally.
The project used standardized methods to quantify disease burdens attributable to
26 environmental, occupational, behavioral, and lifestyle risk factors in 2000 and at
selected future times up to 2030. The disease burden is the total amount of disease
or premature death within the population. Comparing fractions of the disease burden
attributable to several different risk factors requires (1) knowledge of the
severity/disability and duration of the health deficit and (2) the use of standard units
of health deficit. For this purpose, the project used the disability-adjusted life year
(DALY), which is the sum of Years of life lost due to premature death (YLL), and
Years of life lived with disability (YLD). YLL takes into account the age at death; YLD
85
takes into account disease duration, age at onset, and a disability weight reflecting
the severity of disease.
Comparing the attributable burdens for specific risk factors required
knowledge of (1) the baseline burden of disease, absent the particular risk factor; (2)
the estimated increase in risk of disease/death per unit increase in risk factor
exposure (the ―relative risk‖); and (3) the current or estimated future population
distribution of exposure. The avoidable burden was estimated by comparing
projected burdens under alternative exposure scenarios.
The global assessment used WHO estimates of the baseline burden of
climate-sensitive diseases (diseases included were cardiovascular deaths associated
with thermal extremes, diarrhea episodes, cases of malaria, malnutrition and deaths
in natural disasters). Existing and new models were used to quantify the effect of
climate variations on each of these outcomes (the relative risk), taking into account
adaptation to changing conditions and potentially protective effects of socio-
economic development. The HADCM2 climate model produced by the Hadley
Centre (United Kingdom) gave the population distribution of exposure; this model
describes future climate under various scenarios of greenhouse gas emissions.
Climate change was expressed as the change in climatic conditions relative to those
observed in the reference period 1961–1990.
Disease burdens were estimated for five geographical regions and for
developed countries. The attributable disease burden was estimated for 2000. The
climate-related relative risks of each health outcome under each climate change
scenario, relative to the situation if climate change does not occur, were estimated for
2010, 2020 and 2030. The results give a first indication of the potential magnitude
and distribution of some of the health effects of climate change.
Taking a comparative risk assessment approach requires data on the burden
of climate-sensitive diseases, exposure–response relationships for these diseases
across a range of ambient temperatures (and other weather variables) and the ability
to link these with population, climate and socio-economic scenarios. Therefore, this
approach may be difficult to apply where data or expertise in these methods are
limited.
86
b. Disease-specific models
Predictive models of the health impacts of climate change use different
approaches to classify the risk of climate-sensitive diseases. For malaria, results
from predictive models are commonly presented as maps of potential shifts in
distribution attributed to climate change. The models are typically based on climatic
constraints on the development of the vector and parasite; maps generated identify
potential geographic areas of risk, but do not provide information on the number of
people who may be at risk within these areas. Few predictive models incorporate
adequate assumptions about other determinants of the range and incidence of
disease, such as land-use change or prevalence of drug resistance for malaria, or
about adaptive capacity.
i. MARA/ARMA
Malaria is a disease caused by four different strains of Plasmodium
carried by a variety of Anopheles mosquitoes. The most serious form of
malaria is caused by Plasmodium falciparum. Approximately 48% of the
world population remains exposed to the risk of malaria.
A country-level model was developed to show how the range of stable
falciparum malaria in Zimbabwe could change under different climate change
scenarios. Zimbabwe was chosen because it has areas where the climate is
suitable for endemic malaria transmission, areas where transmission is
absent and areas where the climate is occasionally suitable, resulting in
epidemics. The model was based on MARA/ARMA (Mapping Malaria Risk in
Africa/Atlas du Risque de la Malaria en Afrique), which mapped and modeled
the current distribution of malaria in sub-Saharan Africa.
MARA/ARMA uses three variables to determine climatic suitability for
a particular geographic location: mean monthly temperature, winter minimum
temperature and total cumulative monthly precipitation. The MARA/ARMA
decision rules were developed using fuzzy logic to resolve the uncertainty in
defining distinct boundaries to divide malarious from non-malarious regions.
Temperature is a major factor determining the distribution and incidence of
malaria. Temperature affects both the Plasmodium parasite and the
Anopheles mosquito, with thresholds at both temperature extremes limiting
the survival or development of the two organisms. Anopheles must live long
enough to bite an infected person, allow the parasite to develop and then bite
87
a susceptible human. The lower temperature threshold of 18 C is based on
the time required for parasite development and length of mosquito
survivorship at that temperature; below 18C few parasites can complete
development within the lifetime of the mosquito. The mosquito survivorship
rate peaks at 31C. At this point, less than 40% of the mosquitoes survive
long enough for the parasite to complete its development cycle. As
temperatures rise above 32C, the mosquito‘s probability of survival
decreases. Higher temperatures, however, enable the mosquitoes to
digest blood meals more rapidly, which in turn increases the rate at
which they bite. This increased biting rate coupled with faster
development of the parasite leads to increased infective bites for those
mosquitoes that do survive. The upper temperature threshold for both
mosquitoes and larvae to survive is 40C.
The COSMIC programme was used to generate Zimbabwe-specific
scenarios of climate change that were then used as inputs to MARA/ARMA to
generate maps of future transmission potential. The same approach can be
applied in other countries covered by MARA/ARMA as long as a geographic
information system is available. Data from MARA/ARMA are readily available
and the COSMIC program is free. A major limitation of the model is that it
includes only climate and not other drivers of malaria transmission, including
land-use change and drug resistant parasites. The output is maps of future
transmission potential, not projected numbers of cases.
ii. MIASMA
MIASMA (Modeling Framework for the Health Impact Assessment of
Man-Induced Atmospheric Changes) includes modules for (1) vector-borne
diseases, including malaria, dengue fever and schistosomiasis; (2) thermal
heat mortality; and (3) ultraviolet (UV)-related skin cancer due to stratospheric
ozone depletion. The models are driven by population and
climate/atmospheric scenarios, applied across baseline data on disease
incidence and prevalence, climate conditions and the state of the
stratospheric ozone layer. Outputs are:
(1) For vector-borne disease modules, cases and fatalities from
malaria, and incident cases for dengue fever and schistosomiasis;
88
(2) for the thermal stress module, cardiovascular, respiratory and total
mortality; and (3) for skin cancer module, malignant melanoma and non-
melanoma skin cancer.
Climate input is module or disease specific. For the vector-borne
diseases, maximum and minimum temperature and rainfall are required, as
well as other baseline data determined by local experts. For example, for
malaria it would help to know the level of partial immunity in the human
population and the extent of drug resistant malaria in the region. For thermal
stress, maximum and minimum temperatures are required. For skin cancer,
the column entitled loss of the stratospheric ozone over the site is required to
determine the level of UV-B radiation potentially reaching the ground.
iii. CIMSiM and DENSiM
These models are designed to determine the risk of dengue fever.
CIMSiM is a dynamic life-table simulation entomological model that produces
mean-value estimates of various parameters for all cohorts of a single
species of Aedes mosquito within a representative area. Because
microclimate is a key determinant of vector survival and development for all
stages, CIMSiM contains an extensive database of daily weather information.
DENSiM is focused on current control measures and requires field surveys to
validate some of the data. DENSiM (Focks et al., 1995) is essentially the
corresponding account of the dynamics of a human population driven by
country- and age-specific birth and death rates. The entomological factors
passed from CIMSiM are used to create the biting mosquito population. An
infection model accounts for the development of the virus within individuals
and its passage between the vector and human populations.
Inputs into DENSiM are a pupal/demographic survey to estimate
the productivities of the various local water-holding containers, and
daily weather values for maximum/minimum temperature, rainfall and
saturation deficit. The parameters estimated by DENSiM include
demographic, entomologic, serologic and infection information on a
human age-class and/or time basis.
89
c. Qualitative assessment
Potential future health risks of climate change can be estimated from
knowledge of the current burden of climate-sensitive diseases, the extent of control
of those diseases and how temperature and precipitation can affect the range and
intensity of disease. For example, is highland malaria a current problem? What is
the extent of that problem? How well is the disease controlled during epidemics?
How could the burden of disease be affected if temperature increased so that the
vector moved up the highlands? Similarly, future risks can be estimated from
relationships used in the WHO Global Burden of Disease project.
C. Outputs of the V&A Tools
1. Projected 2020 and 2050 Cases of Selected Diseases
In projecting the 2020 and 2050 cases of the climate change-related diseases, the PAGASA
projection data for rainfall, maximum temperature and relative humidity were used in the
models (Table 9).
Table 9 Projected Climate Change Data in 2020 and 2050
Month Daily Rainfall Monthly Rainfall Daily Max. Temperature Daily Relative Humidity
2020 2050 2020 2050 2020 2050 2020 2050
Jan 0.00 0.00 0.00 0.00 31.30 32.60 72.95 72.33
Feb 0.40 0.10 12.40 3.10 32.30 33.70 72.94 72.11
Mar 0.50 0.20 15.50 6.20 34.20 36.30 64.13 63.27
Apr 0.70 0.40 21.00 12.00 36.60 38.00 61.25 60.84
May 2.10 1.70 65.10 52.70 37.60 38.70 66.47 64.79
Jun 15.90 9.20 477.00 276.00 33.10 36.30 72.95 72.58
Jul 25.40 16.90 787.40 523.90 32.00 34.20 80.08 79.82
Aug 38.20 21.90 1146.00 657.00 30.30 32.80 73.05 72.77
Sept 27.60 13.60 855.60 421.60 31.50 33.10 72.73 72.39
Oct 2.30 18.80 69.00 564.00 33.10 32.60 79.64 78.81
Nov 0.70 6.90 21.70 213.90 32.70 32.90 66.13 65.56
Dec 6.30 7.00 195.30 217.00 30.40 31.40 88.44 87.64
Total 120.10 96.70 3666.00 2947.40 395.10 412.60 870.76 862.92
90
Average 10.00833 8.05833 305.5 245.617 32.925 34.38333 72.56308 71.9097
Data Source: PAGASA, 2010
How many disease cases would there be in 2020 without any adaptation measures or
interventions? The impact models for dengue and cholera in NCR could be used to predict
the number of cases of these diseases. The predicted values or number of cases of dengue
and cholera are presented in the following sections.
Dengue Cases Projection
Using the projected climate change data in 2020 by the PAGASA, plug into the impact model
for dengue cases, the projected dengue cases in NCR by 2020 will have a total of 2,128
cases (Table 10).
Table 10 Dengue Cases Projection, 2020
Month Cases Rainfall (mm/mo)
Max. Temp (°C)
Relative Humidity
%
JAN 357 0 31.3 72.9
FEB 327 12.4 32.3 72.9
MAR 8 15.5 34.2 64.1
APR negative 21 36.6 61.2
MAY negative 65.1 37.6 66.5
JUN 25 477 33.1 73.0
JUL 82 787.4 32 80.1
AUG negative 1146 30.3 73.1
SEP negative 855.6 31.5 72.7
OCT 486 69 33.1 79.6
NOV 99 21.7 32.7 66.1
DEC 743 195.3 30.4 88.4
Total 2128 3666
In 2050, there will be 1,735 cases of dengue (Table 11). This is 18.4% lower than the 2020
projection. This difference is explained by the reduction in average monthly rainfall and
increase in the average monthly maximum temperature as well as reduction in the average
monthly relative humidity.
91
Table 11 Dengue Cases Projection, 2050
Month Cases Rainfall
(mm)
Max. Temp (°C)
Relative Humidity
JAN 310 0 32.6 72.3
FEB 277 3.1 33.7 72.1
MAR negative 6.2 36.3 63.3
APR negative 12 38 60.8
MAY negative 52.7 38.7 64.8
JUN 69 276 36.3 72.6
JUL 189 523.9 34.2 79.8
AUG negative 657 32.8 72.8
SEP 41 421.6 33.1 72.4
OCT 166 564 32.6 78.8
NOV negative 213.9 32.9 65.6
DEC 683 217 31.4 87.6
Total 1735 2947.4
Cholera Cases Projection
It is projected that there will be 143 cases of cholera in 2020 (Table 12) using the impact
model parameters.
Table 12 Cholera Cases Projection, 2020
Month Cases Rainfall
(mm)
Max. Temp (°C)
Relative Humidity
JAN 5 0.00 31.30 72.95
FEB 3 12.40 32.30 72.94
MAR negative 15.50 34.20 64.13
APR negative 21.00 36.60 61.25
MAY negative 65.10 37.60 66.47
JUN 14 477.00 33.10 72.95
JUL 29 787.40 32.00 80.08
AUG 36 1146.00 30.30 73.05
SEP 26 855.60 31.50 72.73
OCT 8 69.00 33.10 79.64
NOV negative 21.70 32.70 66.13
DEC 22 195.30 30.40 88.44
Total 143 3666.00
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In 2050, there will be 99 cases of cholera (Table 13). This is a 31.06% reduction in number
of cases from 2020 due to a 19.6% reduction in total rainfall, increase in maximum
temperature and reduction in average relative humidity.
Table 13 Cholera Cases Projection, 2050
Month Cases Rainfall
(mm) Max.
Temp (°C) Relative Humidity
JAN 2 0.00 32.60 72.33
FEB 0 3.10 33.70 72.11
MAR negative 6.20 36.30 63.27
APR negative 12.00 38.00 60.84
MAY negative 52.70 38.70 64.79
JUN 3 276.00 36.30 72.58
JUL 18 523.90 34.20 79.82
AUG 19 657.00 32.80 72.77
SEP 12 421.60 33.10 72.39
OCT 21 564.00 32.60 78.81
NOV 3 213.90 32.90 65.56
DEC 20 217.00 31.40 87.64
Total 99 2947.40
Malaria Cases
Malaria is projected to have 258 cases in 2020 (Table 14). Its predictors are monthly rainfall
and maximum temperature.
Table 14 Malaria Cases Projection, 2020
Month Cases Rainfall
(mm)
Max. Temp (°C)
JAN 19 0 31.30
FEB 26 12.4 32.30
MAR 40 15.5 34.20
APR 58 21 36.60
MAY 61 65.1 37.60
JUN negative 477 33.10
JUL negative 787.4 32.00
AUG negative 1146 30.30
SEP negative 855.6 31.50
OCT 27 69 33.10
NOV 28 21.7 32.70
DEC negative 195.3 30.40
93
Month Cases Rainfall
(mm)
Max. Temp (°C)
Total 258 3666 395.10
In 2050, malaria is projected to increase to 308 cases (Table 15). This is due to the
reduction in rainfall and increase in temperature.
Table 15 Malaria Cases Projection, 2050
Month Cases
2020 Rainfall
(mm)
Max. Temp (°C)
JAN 29 0 32.60
FEB 37 3.1 33.70
MAR 57 6.2 36.30
APR 69 12 38.00
MAY 71 52.7 38.70
JUN 33 276 36.30
JUL negative 523.9 34.20
AUG negative 657 32.80
SEP negative 421.6 33.10
OCT negative 564 32.60
NOV 12 213.9 32.90
DEC 1 217 31.40
Total 308 2947.4
The methodology of impact modeling was presented and discussed sequentially in the paper
so that planners may be guided in case they would like to use the same procedure.
Likewise, the process used in screening the models follows the most stringent scientific
statistical tools used in testing the validity of the models as well as their specific elements. All
models that did not pass the tests were discarded. The accepted models that passed
through the screening process were the ones presented in this study.
The models may be used under the following conditions:
1. The models can be used nationwide provided that the climate change conditions are
similar to those areas where the models were developed. While the models for
predictive purposes may still be used for instance in 2020 and 2050 projections
caution should be exercised when the predictors are beyond the most probable
values.
94
2. For other provinces, they may want to test the models with their health data for the
last five years, and feed these data to the models to test their goodness of fit using
Chi-Square Test. If there is no difference between the health data and the predicted
values, use the model in that province. Otherwise, the province should opt to develop
its own disease impact models using its provincial data.
General vs Specific Model Applications2
As a background, the models were based on data from the NCR, Palawan,
Pangasinan and Rizal. The health data from Pangasinan and Rizal were not useful because
of insufficiency and therefore not suitable for modeling purposes. Most of the data were on
alert thresholds and epidemic thresholds3, which according to DOH experts were not useful
because such data were projected on actual cases. Data on actual cases for most of the
diseases were not readily available.
Due to the definition of alert and epidemic thresholds, the study searched for
alternative data where disease cases are available. These data were found available from
the NCR. With the exception of malaria cases in Palawan, the models on alert and epidemic
thresholds were totally abandoned. Instead new models were developed based only on
dengue and cholera cases from NCR because these were the diseases that showed positive
correlations to CC indicators. Because the data used came from NCR, Palawan, Pangasinan
and Rizal, the models developed appeared as site specific models. Let us look deeper into
this.
Disease vectors are certain species of mosquitoes for malaria and dengue, animals
mostly rats for leptospirosis, and bacteria/virus as pathogens for cholera and typhoid. All
these vector species and pathogens have their own biological and environmental limits of
habitat where they grow well and thrive. The limiting factors are generally environmental
factors such as temperature, rainfall and relative humidity and the conditions on the ground
where they live. Beyond the environmental limits of disease vectors and pathogens, they will
die until the vectors and pathogens mutate and adapt to a new threshold level of
environmental conditions where they could still survive. This means therefore that the
2 A question during the peer review was raised that the equation/s used in the model projections are too site
specific and may prove to be difficult to use by national government agencies. Also, it would be more pragmatic if the model to be chosen would yield the highest projection or worst case scenario as this would prove to be more useful in local CC planning and programming.
3 Alert threshold and epidemic threshold according to DOH experts are based on certain units of standard
deviations from the mean of actual diseases cases. So that if there are no actual cases, there should be no estimates of alert threshold and epidemic threshold.
95
mosquitoes carrying malaria in Cagayan valley has similar environmental threshold level in
Cagayan De Oro and in other places in the country. If such environmental thresholds are
not present in an area, then malaria-carrying mosquitoes will not thrive and therefore no
malaria cases. The same is true with dengue, leptospirosis, typhoid and cholera. In the
case of leptospriosis where rats are the major vector, they live in moist areas close to
garbage sites and sewerage system. If their habitats are changed, meaning cleaned and the
temperature is increased, they will look for an area where the condition is still moist and dirty
environment. In the case of typhoid and cholera, if water and food sources are contaminated
within a certain environmental threshold level, the cholera will survive and will continue to
infect vulnerable people.
If the environmental conditions conducive for vector growth are present anywhere in
any barangay, municipality and province in the country, the diseases have the potential to
occur in such areas.
Thus, since the models were based on environmental conditions defined by
temperature, rainfall and relative humidity for each of the studied diseases, the models
therefore can be used in either specific sites or in any municipality, province or nationwide as
long as the climate change indicators are given in each of these areas or in general the
whole country. The limiting assumption is that the vectors and pathogens will not mutate to
adapt to the changing environment as there is no data available to forecast mutation impact.
2. Cost Implications of Disease Impacts in 2020 and 2050
Background
Climate change is expected to cause the emergence of various diseases and a rise
in disease prevalence and intensity. As guide for effective local governance, particularly in
decision making in terms of resource allocation for the reduction of risks and future threats
associated with diseases arising from climate change, it is important to come up with
estimates of the costs associated with these diseases.
This study component aimed to determine the following:
a) Cost implications of the diseases: dengue, malaria, leptospirosis, cholera, and
typhoid in NCR, Palawan, Pangasinan, and Rizal in terms of diagnosis,
96
treatments, income loss of affected individuals, and prevention measures based
on results of previous studies; and
b) Percentages of costs over the provincial incomes of the three provinces.
Methodology
The study used the benefit transfer approach, wherein secondary economic data
from similar studies were adopted and used in the calculations of the cost impacts of the
diseases. The economic data used for dengue were derived from the study by Borja M,
Lorenzo FM et al. in 2007, titled ―Burden of Disease and Economic Evaluation of Dengue‖
(unpublished). On the other hand, the economic data for malaria were lifted from the study
of Lorenzo FM, Rivera P, et al. in 2004, titled, ―Burden of Disease and Economic Evaluation
of Malaria‖ (unpublished). The previous studies utilized cost-effectiveness analysis.
Since these studies were conducted in 2007 and 2004, the future costs data for
dengue and malaria were projected to 2020 and 2050 by applying an average interest rate of
4.3% per annum (NSO, 2010).
Using the disease impact models, the projected costs were applied to the projected
―cases‖, ―alert thresholds‖, and ―epidemic thresholds‖ of dengue and malaria in 2020 and
2050 for Palawan and Pangasinan. There were no economic data and projected cases, alert
thresholds and epidemic thresholds for Rizal province. Thus, no economic analysis was
done.
The cost parameters considered for dengue were costs of diagnoses and treatments,
income losses, and cost of preventive measures. Generally, the costs of diagnoses and
treatments of dengue are charged to the budget of the provincial government while income
losses are shouldered by the infected persons. Income taxes, however, that accrue to the
government are not accounted for in this study but it can be determined by applying 10%
income tax rate to the income losses.
For malaria, the costs of diagnoses and treatments of hospitalized cases due to
individual vectors and preventive measures were computed using the same annual growth
increment of 4.3% up to years 2020 and 2050.
To estimate how much net savings the provincial governments of Palawan and
Pangasinan would have in 2020 and 2050, the costs of preventive measures computed for
2020 and 2050 were applied and deducted from the costs of diagnoses and treatments for
the same periods.
97
NATIONAL CAPITAL REGION
Costs of Dengue
Dengue cases, 2020. Table 16 presents the monthly and total cases and monthly and
annual costs for dengue diagnoses, treatments and income losses. The funds required for
diagnoses and treatments are expected to come from the provincial government, while
income loss is an estimate of benefit foregone from those affected by the disease and as
well as taxes that would accrue to the government. There will be projected 1,735 cases of
dengue in NCR in 2020. This will require a total cost of PhP 9.3 M for diagnosis and
treatment cost of PhP 4.5 M and a total income loss of PhP 1.3 M from families that will be
affected. The total cost of dengue will be PhP15.1 Mil. The percentages of monthly cost for
diagnosis, treatment and income loss over total are given in the table. The total cost of
diagnosis is 54% of the total cost of dengue, treatment cost is 31% and income loss is 15%.
Table 16 Projected Dengue Costs (PhP), NCR, 2020
Month
Dengue
Cases
Cost of Dengue Cases in 2020
Monthly Cost of
Diagnosis
% Over Grand Total
Monthly Treatment
Cost
% Over Grand Total
Monthly Income
Loss
% Over Grand Total
Grand Total
Cost in 2007
2531 1223 357 4111
Cost in 2020
4375.1 2114.09 617.11 7106.3
JAN 357
1,560,681.57
754,136 220,135 2,534,953
FEB 327
1,432,438.49
692,168 202,046 2,326,653
MAR 8
34,833.37
16831.8185 4913.2646 56,578
APR negative
MAY negative
JUN 25
109,906.04
53,108 15,502 178,516
JUL 82
358,100.38
173,038 50,510 581,648
Aug negative
SEP negative
-
OCT 486
2,127,359.17
1,027,960 300,065 3,455,384
NOV 99
433,776.55
209,604.96
61,184.40
704,566
DEC 743
3,251,177.22
1,571,000 458,580 5,280,757
98
Month
Dengue
Cases
Cost of Dengue Cases in 2020
Monthly Cost of
Diagnosis
% Over Grand Total
Monthly Treatment
Cost
% Over Grand Total
Monthly Income
Loss
% Over Grand Total
Grand Total
Total 2128 9,308,273 61.57 4,497,846 29.75 1,312,936 8.68 15,119,055
Basic data source: Borja M, Lorenzo FM et al. (2007) in their study entitled ― Burden of Disease and Economic Evaluation of Dengue‖ (unpublished)
Note: Dengue cases, alert thresholds and epidemic thresholds were projected using the impact models.
Costs were projected using a 4.3% annual growth rate (NSO, 2010)
Blank cells indicate negative figures.
Dengue Cases, 2050. Table 17 presents the dengue cases and the corresponding costs of
diagnoses, treatments and income loss in 2050. Comparison between the 2020 and the
2050 cost figures shows that there would be a substantial increase in the cost of dengue
from PhP 15.11 M to PhP 43.6 M. This increase is not due to increase in dengue cases but
to the cost of money.
Table 17 Projected Dengue Costs (PhP), NCR, 2050
Month
Dengue
Cases
Cost of Dengue Cases in 2050
Monthly Cost of
Diagnosis
% Over Grand Total
Monthly Treatment
Cost
% Over Grand Total
Monthly Income
Loss
% Over Grand Total
Grand Total
Cost in 2007
2531 1223 357 4111
Cost in 2050
15470.97
7475.7
2182.19 25128.86
JAN 310 4,796,001 2,317,467 17.86744 676,479 7,789,947
FEB 277 4,285,459 2,070,769 15.96542 604,467 6,960,694
MAR negative
APR negative
MAY negative
JUN 69 1,067,497 515,823 3.976945 150,571 1,733,891
JUL 189 2,924,013 1,412,907 412,434 4,749,355
AUG negative
SEP 41 634,310 306,504 89,470 1,030,283
OCT 166 2,568,181 1,240,966 362,244 4,171,391
NOV negative
DEC 683 10,566,673 5,105,903 1,490,436 17,163,011
Total 1735 26,842,133 61.57 12,970,340 29.75 3,786,100 8.68 43,598,572
Basic data source: Borja M, Lorenzo FM et al. (2007) in their study entitled ― Burden of Disease and Economic Evaluation of Dengue‖ (unpublished)
Note: Dengue cases, alert thresholds and epidemic thresholds were projected using the impact models.
Costs were projected using a 4.3% annual growth rate (NSO, 2010)
Blank cells indicate negative figures.
99
Cost of Preventing Dengue
NCR, 2020. What if the government of NCR would want to save the funds intended for the
diagnosis and treatment of dengue in 2020? What would be its courses of action and how
much should it invest by that period? Answers to these questions are shown in Table 18.
There are three important actions that should be done. These are fogging, larval survey, and
ovitrap. The corresponding costs for these preventive measures are presented in the table.
Table 18 Cost of Preventive Measures of Dengue, NCR, 2020.
Preventive Measure Cost/HH (2007)
Cost/HH (2020)
Frequency of Application (times/year)
Total
Fogging 330 570.44 4 1,055,562.02
Larval Survey 224 387.21 4 716,502.60
Ovitrap 326 563.53 4 1,042,767.19
Total 880 1,521 2,814,831.80
Note:
Total population (2000)
9,932,560
Total population (2020)
12,038,263
Household size 4.6
Annual growth rate 1.06
Number of Persons affected in 2020 2128
Equivalent number of households
463
Basic data source: Borja M, Lorenzo FM et al (2007) in their study entitled “ Burden of Disease and Economic Evaluation of Dengue” (unpublished).
Costs were projected with a 4.3% annual cost increase (NSO, 2010). http://www.census.gov.ph/ncr/ncrweb/NCR%20quickstat/manila_summary.html
NCR, 2050. Preventing dengue in 2050 using the same preventive measures would need a
total of PhP 1.9 million for alert threshold and PhP 3.7 million for epidemic threshold (Table
19).
Table 19 Cost of Preventive Measures of Dengue, NCR, 2050.
Preventive Measure
Cost/HH (2007)
Cost/HH (2050)
Frequency of
Application (times/year) Total
Fogging 330 2017 4 3,043,039.13
100
Larval Survey
224 1369 4 2,065,404.35
Ovitrap 326 1993 4 3,006,830.43
Total 880 5379 8,115,273.91
Note:
Population (2000)
9,932,560
Population (2050)
15,196,817
Household size 4.6
Annual growth rate 1.06
Number of Persons affected in 2050 1,735.00
Equivalent number of households 377.173913
Number of households that would be affected in 2050 epidemic threshold 173
Basic data source: Borja M, Lorenzo FM et al (2007) in their study entitled “ Burden of Disease and Economic Evaluation of Dengue (unpublished)”
Note: Population in 2050 was projected based on the 2000 population data with 3.6% growth rate and 4.98 household size (NSO,2010).
Costs were projected with a 4.3% annual cost increase (NSO, 2010).
Net Savings from Preventing Dengue
Applying effective preventive measures against dengue would result in significant
savings on the part of the provincial government in the amounts of PhP 10.9 M in 2020 and
PhP 31.69 M in 2050. (Table 20)
Table 20 Net savings from preventing Dengue
Cost Item
2020 2050
PhP PhP
Diagnosis 9,308,272.79
26,842,132.95
Treatment 4,497,846.09
12,970,339.50
Cost of Preventive Measures
2,814,831.80
8,115,273.91
Net Savings 10,991,287.08
31,697,198.54
101
PALAWAN
Costs of Malaria
Malaria Cases, Palawan, 2020. Table 21 shows the projected monthly cases of malaria in
Palawan, cost of diagnosing malaria in hospital, total cost of treatment, cost of falciparum
treatment, cost of vivax, cost of treatment for additional drugs in hospitalized cases, and
treatment cost of sequelae. For a total of 187 projected cases of malaria in Year 2020, the
total fund requirement (total cost for diagnosis plus the total cost of treatment) would be PhP
0.68 M.
Table 21 Projected Cost (PhP) of Malaria Cases, Palawan, 2020
Month Cases
Costs of diagnosis (hospital) per case
Total costs of treatment
Cost of treatment P. falciparum
Cost of treatment P. vivax
Total cost of treatment for
Additional Drugs (hospitalized
cases)
Total cost of treatment for
sequelae
Cost in 2004
1224.9 627.6 62.6 12.7 327.3 224.9
Cost of 2020
2402.4 1230.9 122.9 25 641.9 441.10
JAN 17 40,841.58 20,925.31 2088.89 425.11 10911.83 7,498.72
FEB 20 48,048.92 24,618.01 2457.52 500.13 12837.45 8,822.03
MAR 24 57,658.70 29,541.61 2949.02 600.16 15404.94 10,586.43
APR 29 69,670.93 35,696.11 3563.4 725.19 18614.3 12,791.94
MAY 22 52,853.81 27,079.81 2703.27 550.15 14121.19 9,704.23
JUN 15 36,036.69 18,463.51 1843.14 375.1 9628.09 6,616.52
JUL 16 38,439.13 19,694.41 1966.02 400.11 10269.96 7,057.62
AUG 8 19,219.57 9,847.20 983.01 200.05 5134.98 3,528.81
SEP 14 33,634.24 17,232.61 1720.26 350.09 8986.21 6,175.42
OCT 6 14,414.68 7,385.40 737.26 150.04 3851.23 2,646.61
NOV 10 24,024.46 12,309.00 1228.76 250.07 6418.72 4,411.01
DEC 6 14,414.68 7,385.40 737.26 36.65 3851.23 2,646.61
Total 187 449,257.38 230,056.74 22,977.81 4,562.85 120,030.16 83,151.95
Data source: Lorenzo FM, Rivera P, et al (2004). Burden of Disease and Economic Evaluation of Malaria. Costs were projected with a 4.3% annual cost increase (NSO, 2010).
Cost of Preventing Malaria
Malaria Cases, Palawan, 2020. The projected amount required to prevent malaria diseases
in Palawan is PhP 0.29 M in Year 2020 (Table 22)
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Table 22 Projected Cost (PhP) of Malaria Prevention, 2020, Palawan
Month 2020
Cases
Cost of using insecticide treated bed nets (ITN)
Cost of focal spraying
Costs of early
diagnosis
Costs of early
treatment Total
Cost in 2004 per case 50.1 716.2 6.7 3.1 776.1
Cost in 2020 per case 98.3 1404.6 13.2 6.1 1522.2
JAN 17 1,670.46 23,878.55 224.5 103.84 25,877.35
FEB 20 1,965.24 28,092.41 264.11 122.17 30,443.93
MAR 24 2,358.29 33,710.89 316.94 146.6 36,532.72
APR 29 2,849.60 40,733.99 382.97 177.14 44,143.70
MAY 22 2,161.77 30,901.65 290.53 134.38 33,488.33
JUN 15 1,473.93 21,069.31 198.09 91.63 22,832.96
JUL 16 1,572.20 22,473.93 211.29 97.73 24,355.15
AUG 8 786.1 11,236.96 105.65 48.87 12,177.58
SEP 14 1,375.67 19,664.69 184.88 85.52 21,310.76
OCT 6 589.57 8,427.72 79.23 36.65 9,133.17
NOV 10 982.62 14,046.20 132.06 61.08 15,221.96
DEC 6 589.57 8,427.72 79.23 36.65 9,133.17
Total 187 18,375.04 262,664.03 2,469.47 1,142.27 285,978.00
Data source: Lorenzo FM, Rivera P, et al (2004). Burden of Disease and Economic Evaluation of Malaria (Unpublished) Costs were projected with a 4.3% annual cost increase (NSO, 2010).
Malaria Cases, Palawan, 2050. Table 23 presents the projected monthly malaria cases and
the costs of diagnosis and treatments. The projected 185 malaria cases in 2050 are a bit
less compared to the 2020 figures (187) but the required costs are significantly higher. This
is due to the cost of money which increased the funding requirement in 2050. The provincial
government would require a total budget of PhP2.38 M for the diagnoses and treatment of
malaria in 2050, compared to PhP 0.68 M in 2020.
Table 23 Projected Costs of Malaria, Palawan, 2050
Month Cases Costs of
diagnosis (hospital)
Total costs of treatment
Cost of treatment P. falciparum
Cost of treatment P.
vivax
Total cost of treatment for
Additional Drugs (hospitalized
cases)
Total cost of treatment for
sequelae
Cost in 2004
1224.9 627.6 62.6 12.7 327.3 224.9
Cost in 2050
8495.4 4352.6 434.5 88.4 2269.7 1,559.80
JAN 22 186,898.38 95,757.96 9559.14 1945.39 49934.5 34,315.50
FEB 25 212,384.53 108,815.86 10862.66 2210.67 56743.75 38,994.89
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Month Cases Costs of
diagnosis (hospital)
Total costs of treatment
Cost of treatment P. falciparum
Cost of treatment P.
vivax
Total cost of treatment for
Additional Drugs (hospitalized
cases)
Total cost of treatment for
sequelae
MAR 28 237,870.67 121,873.76 12166.18 2475.96 63553 43,674.27
APR 33 280,347.58 143,636.93 14338.71 2918.09 74901.75 51,473.25
MAY 22 186,898.38 95,757.96 9559.14 1945.39 49934.5 34,315.50
JUN 13 110,439.95 56,584.25 5648.58 1149.55 29506.75 20,277.34
JUL 15 127,430.72 65,289.52 6517.6 1326.4 34046.25 23,396.93
AUG 4 33,981.52 17,410.54 1738.03 353.71 9079 6,239.18
SEP 12 101,944.57 52,231.61 5214.08 1061.12 27237 18,717.55
OCT 1 8,495.38 4,352.63 434.51 88.43 2269.75 1,559.80
NOV 7 59,467.67 30,468.44 3041.55 618.99 15888.25 10,918.57
DEC 3 25,486.14 13,057.90 1303.52 265.28 6809.25 4,679.39
Total 185 1,571,645.50 805,237.36 80,383.70 16,358.99 419,903.74 288,562.15
Data source: Lorenzo FM, Rivera P, et al (2004). Burden of Disease and Economic Evaluation of Malaria Costs were projected with a 4.3% annual cost increase (NSO, 2010).
Cost of Malaria Prevention
Malaria Cases, Palawan, 2050. The preventive measures against malaria are: a) use of
treated bed nets; b) focal spraying; c) early diagnosis; and d) early treatment. These
measures, properly undertaken in 2050 with the right timing, would cost the provincial
government of Palawan a total of PhP 0.99 M only (Table 24).
Table 24 Cost Analysis Projections of Malaria Control Strategies, Palawan, 2050
Month 2020
Cases
Cost of using insecticide treated bed nets (ITN)
Cost of focal spraying
Costs of early diagnosis
Costs of early treatment Total
Cost in 2004 50.1 716.2 6.7 3.1 776.1
Cost in 2050 347.5 4966.9 46.7 21.6 5382.7
JAN 22 7,644.32 109,272.51 1027.34 475.2 118419.37
FEB 25 8,686.72 124,173.31 1167.43 540 134567.46
MAR 28 9,729.13 139,074.11 1307.52 604.8 150715.56
APR 33 11,466.47 163,908.77 1541.01 712.8 177629.05
MAY 22 7,644.32 109,272.51 1027.34 475.2 118419.37
JUN 13 4,517.10 64,570.12 607.06 280.8 69975.08
JUL 15 5,212.03 74,503.99 700.46 324 80740.48
AUG 4 1,389.88 19,867.73 186.79 86.4 21530.8
SEP 12 4,169.63 59,603.19 560.37 259.2 64592.39
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OCT 1 347.47 4,966.93 46.7 21.6 5382.7
NOV 7 2,432.28 34,768.53 326.88 151.2 37678.89
DEC 3 1,042.41 14,900.80 140.09 64.8 16148.1
Total 187 64,281.74 918,882.49 8,638.98 3,996.01 995,799.25
Data source: Lorenzo FM, Rivera P, et al (2004). Burden of Disease and Economic Evaluation of Malaria
Costs were projected with a 4.3% annual cost increase (NSO, 2010).
Net Savings from the Implementation of Preventive Measures for Malaria
The expected savings of the provincial government of Palawan in preventing malaria are
PhP 0.39 M and PhP 1.38 M in 2050 (Table 25).
Table 25 Net savings from preventive measures for malaria.
Cost Item
2020 2050
PhP PhP
Diagnosis + Treatment
679,435.77 2,376,882.86
Cost of Preventive Measures
285,978.00 995,799.25
393,457.77 1,381,083.61 Net Savings
Costs of Leptospirosis, Cholera, and Typhoid
There were no cost data on the diagnoses, treatments, income losses and costs of
prevention for leptospirosis, cholera, and typhoid in NCR, Palawan, Pangasinan, and Rizal.
Thus, no economic analyses were done.
Cost Impact of Diseases to Provincial Income
The study used the income classification of provinces set by the Department of
Finance through Department Order No.23-08 Effective July 29, 2008 (Table 26).
Table 26 Income Classification of Provinces
Class Average Annual Income
First P 450 M or more
Second P 360 M or more but less than P 450 M
Third P 270 M or more but less than P 360 M
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Class Average Annual Income
Fourth P 180 M or more but less than P 270 M
Fifth P 90 M or more but less than P 180 M
Sixth Below P 90 M
Data source: NSCB (2010) website.
NCR, Palawan, Rizal and Pangasinan fall within First Class Provinces each with an
income of more than PhP450 M. The funds and the percentages of costs over the annual
provincial income of PhP450 M are shown in Table 27. Dengue will have 3-7% cost of
diagnosis and treatment over NCR annual income from 2020 to 2050. The % cost of
preventing dengue is from 0.63% (2020) to 1.8% (2050). The % saving will be 2.73% in 2020
and 7.89% in 2050 if the preventive measures will be implemented.
Table 27 Percentages of Disease Costs Over Provincial Annual Income.
Disease Annual Income 2020 %Over Income 2050 %Over Income
NCR, Palawan, Pangasinan, Rizal 450 Mil
Dengue
a. Diagnosis and treatment 15,119,055.00 3.36 43,598,572.00
9.69
b. Preventive 2,814,831.30 0.63 8,115,273.91 1.80
c. Savings 12,304,223.70 2.73 35,483,298.09 7.89
Malaria
a. Diagnosis and treatment 679,435.77 0.15 2,376,882.86
0.53
b. Preventive 285,978.00
0.06 995,799.25 0.22
c. Savings 393,457.77 0.09 1,381,083.61 0.31
Cholera
a. Diagnosis and treatment no data no data
b. Preventive no data no data
c. Savings
Conclusions
Economic impact analyses were accomplished only for dengue and malaria. Other
diseases such as leptospirosis, cholera, and typhoid were not covered in the economic
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analysis due to lack of data. Rizal was not also covered because of lack of both climate
change and economic data on the selected diseases.
The results in NCR and Palawan indicate that malaria and dengue in 2020 would
require about 1% of its annual income for the diagnoses and treatments of malaria and
dengue. In 2050, the allocation for funding for the same diseases would reach no less than
2% of the provincial income.
However, Pangasinan in 2020 would need about 18% of its income only for
diagnoses and treatments of malaria and dengue. In 2050, the budget requirement for both
diseases would be reduced to 4% owing to the reduced number of malaria and dengue
cases, which is not attributed to preventive measures that would be implemented, but to the
changes in the climate indicators. Such climate change nonetheless, may be good from the
point of view of reducing disease occurrence.
Considering the five diseases for budgeting purposes, the provincial government may
allocate in the future roughly a total of 2.5% of the income of Palawan in 2020 and 5% in
2050 assuming that the average cost requirement of each disease would more or less be the
same with malaria and or dengue. On the other hand, Pangasinan would allocate roughly
45% of its income in 2020 to address the five diseases and 20% in 2050.
Considering the substantial savings that could be generated from applying preventive
measures, the two provincial governments may consider investing on financing preventive
measures to lessen the cost impacts of the diseases, thus lessen the burden of the
provincial governments in addressing these diseases.
Both governments should not wait for the diseases to reach epidemic levels before
they address the malaria and dengue outbreaks as well as other diseases that would
emerge and be aggravated by climate change conditions. It is most certainly beneficial to
prevent disease outbreaks before they even emerge.
D. Compendium of Good and Innovative Climate Change
Adaptation Practices
The main objectives of this activity were to identify policy options and measures on
climate change adaptation measures on health that suit Philippine setting and the integration
of these measures for national and local development planning processes. These outputs
were obtained through an intensive literature review, as well as consultation with experts on
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the various diseases and meetings with representatives of health agencies. Validation was
undertaken through the visits in 3 selected provinces namely Palawan, Rizal and
Pangasinan.
Experiences with the different adaptation practices where they have been
implemented were gathered through an extensive search of related literature from the
internet and through presentations of experts on the various diseases. These practices are
being assessed and attempts are also being made to explain successes and failures,
especially the factors that contributed to them. The adaptation strategy were categorized to
address the vulnerabilities identified in the V&A framework, comprising of the following: (a)
individual/family/community; (b) health system and infrastructure; (c) pathogen and vector
factors; (d) socio-economic factors; (e) environmental factors; and (f) health and
environmental policy. The adaptation measures must incorporate capacitating the individual,
family, and/or community to analyze and adequately respond to future climate risks.
1. Adaptation Options Derived from Literature Review
Adaptation is a key response strategy to minimize potential impacts of climate
change. A primary objective of adaptation is the reduction, with the least cost, of death,
disease, disability and human suffering. The ability to adapt to climate change, and
specifically the impacts on health, will depend on many factors including existing
infrastructure, resources, technology, information and the level of equity in different countries
and regions. Cross-sectoral policies that promote ecologically sustainable development and
address the underlying driving forces will be essential in managing health impacts and
adaptation measures. Strategies to deal with the impacts of climate change on health need
intersectoral, and cross-sectoral adaptation measures and collaboration. The health sector
alone, or in limited collaboration with a few sectors, cannot deal with the necessary "primary"
adaptation. Sensitive indicators of "climate-environmental" health impacts are needed to
monitor possible changes at regional and national levels. Capacity building is an essential
step for adaptation and mitigation strategies. This should include education, training and
awareness raising, as well as the creation of legal frameworks, institutions and an
environment that enables people to take well-informed decisions for the long term benefit of
society (IDS, 2006).
Climate change will affect human health and well-being through a variety of
mechanisms: availability of fresh water supplies, efficiency of local sewerage systems, food
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security, changes in food production, security of human dwellings, distribution and seasonal
transmission of vector-borne diseases, and increased frequency and severity of extreme
weather events. Adaptation is a key response strategy to minimize potential impacts of CC.
In health, the primary objective of adaptation is reduction, with the least cost, of death,
disease, disability and human suffering (IDS, 2006).
Vulnerability (of an individual or a country) is a function both of the exposure to
changes in climate and on the ability to adapt to the impacts associated with that exposure.
Causes of population vulnerability to ill-health in the face of environmental stress also
include the level of dependency (such as reliance on others for information, resources, and
expertise) and geographical isolation (as in small island countries)
The primary objective of adaptation is to reduce disease burdens, injuries,
disabilities, suffering and deaths. Many impacts of climate change - including health impacts
- can be reduced or avoided by various adaptations (Smit, 1993; MacIver and Klein, 1999).
The key determinants of health – as well as the solutions – lie primarily outside
the direct control of the health sector. They are rooted in areas such as sanitation and
water supply, education, agriculture, trade, tourism, transport, development and
housing.
ADAPTATION TO CLIMATE CHANGE AND ADAPTABILITY:
Types of Adaptation
Adaptation can be either reactive or anticipatory. Reactive adaptation occurs
after the initial impacts of climate change have appeared, while anticipatory
(or proactive) adaptation takes place before impacts are apparent.
Adaptation can also be classified based on whether the adaptation is
motivated by private or public interests. Private decision-makers include both
individual households and commercial companies, while public interests are
served by governments at all levels (Klein, 2000, Table 28).
A distinction is often made between planned and autonomous adaptation
(Carter et al., 1994). Planned adaptation is the result of a deliberate policy
decision, based on an awareness that conditions have changed or are about
to change and that action is required to return to or maintain a desired state.
Autonomous adaptation involves the changes human systems will undergo in
response to changing conditions, irrespective of any policy, plan or decision.
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Table 28 Types of Adaptation to Climate Change
Anticipatory / Proactive Reactive
Private 1. Purchase of insurance 2. Construction of "disaster"
resistant houses
1. Changes in insurance premiums
Public 1. National disaster insurance fund
2. Urban planning 3. New building codes,
design standards 4. Incentives for relocation 5. Immunization campaigns
1. Monitoring and surveillance
2. Compensatory payments and subsidies
3. Enforcement of building codes, design standards
Note: Adapted from Klein, 2000
The United Nations Framework Convention on Climate Change suggests that anticipatory
adaptation deserves particular attention from the international climate-change community.
Five generic objectives of anticipatory adaptation:
1. Increasing the robustness of infrastructural designs and long-term
investments—for example, by extending the range of temperature or
precipitation, which a system can withstand without failure, and
changing the tolerance of loss or failure (e.g. by increasing economic
reserves or by insurance);
2. Increasing the flexibility of vulnerable managed systems—for example, by
allowing mid-term adjustments (including change of activities or location)
and reducing economic lifetimes (including increasing depreciation);
3. Enhancing the adaptability of vulnerable natural systems—for example, by
reducing other (non-climatic) stresses and removing barriers to
migration (animal or human) (including establishing eco-corridors);
4. Reversing the trends that increase vulnerability ("maladaptation")—for
example, by introducing setbacks for development in vulnerable areas
such as floodplains and coastal zones; and
5. Improving societal awareness and preparedness—for example, by informing
the public of the risks and possible consequences of climate change
and setting up early-warning systems.
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Table 29 Examples of Primary and Secondary Adaptation Measures to Reduce Health
Impacts
Impact Primary adaptive measures Secondary adaptive measures
Heat stress 1. Heatwave warning systems 2. Urban planning
1. Health personnel educated to detect and treat heat stress
Extreme weather
events
1. Disaster preparedness and mitigation 2. Early warning systems 3. Disaster protection measures, such as
"room for the river"
1. Disaster response
Infectious diseases
1. Integrated environmental management 1. Disease surveillance and monitoring
2. Control of vector-, food- and water- borne diseases
Food security
1. International mechanisms of agriculture, trade and finance
2. Seasonal climate forecasting 3. Famine early warning systems 4. National and local agriculture
measures, such as tailored land use planning, avoidance of monocultures, upgraded food storage and distribution systems, conservation of soil moisture and nutrients
1. Monitoring and surveillance 2. Implementation of nutrition
action plans
Water 1. Pollution reduction and pollution control policies
2. Demand management and water allocation policies
3. Waste water treatment 4. Economic and regulatory measures to
increase irrigation efficiency 5. Capacity building
1. Monitoring and surveillance 2. Capacity building
Note: Adapted from Menne, 2000a
Table 30 Adaptation Options to Reduce the Potential Health Impacts of Climate
Change
Adaptation Option
Level No of People that will Benefit
Feasibility Barriers Cost
1. Interagency co-operation
G, R, N +++ ++ ++ +
2. Improvements to public health infrastructure
N,L +++ + + ++
3. Early warning and epidemic forecasting
L ++ ++ + +
4. Support for infectious disease control
N,L ++ +++ + +
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5. Monitoring and surveillance
N,L ++ +++ + +
6. Integrated environmental management
L + ++ + ++
7. Urban design (including transport systems)
L + + ++ ++
8. Housing, sanitation, water quality
L + + + +
9. technologies (e.g. air conditioning)
L + +++ + +
10. Public Education
L +++ +++ + +
G = Global, R= Regional, N = National, L = Local, +++ = large effect, ++ = medium effect, + =
small effect.
Note: Adapted from McMichael et al., forthcoming in IPCC Special Report on Technology Transfer.
Weather forecesating, seasonal forecasting and early warning systems
Through modern meteorological and hydrological advances such as satellites, radar,
and weather prediction models, it is now possible to provide communities threatened
by potential major disasters with information to allow them to take preventive action
in time.
The use of climate forecasts for epidemic prediction needs to be linked with early
warning systems of known epidemic risk factors.
Hot weather watch / warning system has been used in the United States to predict
specific air masses up to 2 days in advance. One an air
mass is classified as oppressive with the likelihood of high mortality, a ―health
warning‖ is issued to the public health authorities, which prepare a public health
response.
The most elementary form of adaptation is to launch or improve monitoring and
surveillance systems.
Control of vector-borne and water-borne diseases (WHO, 2000)
Malaria
Early diagnosis and prompt treatment
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Selective and sustainable adaptation measures including vector control,
early detection and containment or prevention of epidemics
Local capacity building for basic and applied research
Advances in the mapping of malaria using satellite data, validated with
surveillance information, will help target control efforts
Access to anti-malaria drugs
Preventive measures such as bed nets and housing design
Epidemic forecasting
Environmental management
Dengue
Surveillance of vector densities and disease transmission
Developing selective and sustainable vector control, including
preparedness for emergency control
Strengthening local capacity for assessment of the social, cultural,
economic and environmental factors that lead to increased vector
densities and increased transmission of disease
Early diagnosis and prompt treatment of DHF
Research in vector control
Mobilization of other sectors to incorporate dengue control into
their goals and activities
Diseases contracted through public water supplies
1. Access to safe drinking water
2. Boiling
3. Use of submicrometre point-of-use filters may reduce the risk
of waterborne cryptosporidiosis
4. Simple filtration procedure involving the use of domestic
material can reduce the number of vibrios attached to plankton
in raw water
5. Use of 5% calcium hypochlorite solution to disinfect water and
subsequent use of the treated water in a narrow-mouthed jar
produced drinking water from non-potable sources that met
WHO standards for microbiological quality
Enhancing Adaptive Capacity
Intersectoral collaboration
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1. Integrated water management
2. Integrated environmental management
3. Urban planning
Partnerships with the private sector
Technological development and capacity
Adequate expenditure on health care and prevention (On average, health
expenditures in low income countries in 1994 were $16 per capita. In contrast,
average health expenditures in high-income countries were more than $1800 per
capita.)
o In Vietnam, Health workers have succeeded (in the battle against malaria)
through government commitment, increased funding, and the
widespread use of locally-produced low-cost tools. About 12 million
Vietnamese are protected by house spraying and insecticide-
impregnated bed nets. In areas where malaria is endemic, insecticide
impregnation is provided as a public service, free of charge.
Adaptation includes the strategies, policies and measures undertaken now and in the
future to reduce potential adverse health effects. Individuals, communities and regional and
national agencies and organizations will need to adapt to health impacts relating to climate
change. At each level, options range from incremental changes in current activities and
interventions, to translation of interventions from other countries/regions to address changes
in the geographic range of diseases, to development of new interventions to address new
disease threats. The degree of response will depend on factors such as who is expected to
take action; the current burden of climate-sensitive diseases; the effectiveness of current
interventions to protect the population from weather- and climate-related hazards;
projections of where, when and how the burden of disease could change as the climate
changes (including changes in climate variability); the feasibility of implementing additional
cost-effective interventions; other stressors that could increase or decrease resilience to
impacts; and the social, economic and political context within which interventions are
implemented.
Because climate will continue to change for the foreseeable future and because
adaptation to these changes will be an ongoing process, active management of the risks and
benefits of climate change needs to be incorporated into the design, implementation and
evaluation of disease control strategies and policies across the institutions and agencies
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responsible for maintaining and improving population health. In addition, understanding the
possible impacts of climate change in other sectors could help decision makers identify
situations where impacts in another sector, such as water or agriculture, could adversely
affect population health.
In reality, many of the possible measures for adapting to climate change lie primarily
outside the direct control of the health sector. They are rooted in areas such as sanitation
and water supply, education, agriculture, trade, tourism, transport, development and
housing. Inter-sectoral and cross-sectoral adaptation strategies are needed to reduce the
potential health impacts of climate change.
A policy analysis can determine the feasibility of, and priorities among, these options.
When identifying specific measures to implement, it is often informative to list all potential
measures, without regard to technical feasibility, cost or other limiting criteria; this is the
theoretical range of choice (White, 1961). It is a comprehensive listing of all the measures
that have been used anywhere, new or untried measures, plus other measures that can only
be imagined. The list can be compiled from inventory of current practice and experience,
from a search for measures used in other jurisdictions and in other societies, and from a
brainstorming session with scientists, practitioners and affected stakeholders on measures
that might be options in the future. Listing the full range of potential measures provides
policy makers with a picture of measures that could be implemented to reduce a climate-
related risk, and which choices are constrained because of a lack of information or research,
as a consequence of other policy choices, among others.
The general vulnerabilities of the human sector to climate change-related diseases
according to the Vulnerability and Adaptation Framework are shown below:
Table 31 Health Sector Vulnerabilities to Climate Change-related diseases, 2011
Vulnerability Indicator
Dengue Malaria Leptospirosis Cholera Typhoid
Individual, family, community
All ages with poor sanitary practices and facilities, low immune system, poor hygienic practices, low access to sanitary water, lack of health facilities, do not live in climate resistant houses, and are
All ages with poor sanitary practices and facilities, low immune system, poor hygienic practices, do not live in climate-resistant houses and lack health facilities are highly vulnerable.
All ages, families, communities exposed in flood-prone areas where population of rats and other animals who can be disease vectors are high are highly vulnerable.
All ages with water systems and food sources are easily contaminated with septic waste leakages during floods are highly vulnerable.
All ages with ingestion of contaminated food and water that spoiled are highly vulnerable.
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Vulnerability Indicator
Dengue Malaria Leptospirosis Cholera Typhoid
active outdoors especially at dawn and dusk are highly vulnerable
health systems and infrastructure
Highly vulnerable are those that have no or low access to health practitioners, and health facilities such as clinics and hospitals and drug stores including other important medical facilities.
pathogen/vector factors
Communities and households environment that have no proper sanitation, no proper control of animals living near humans, no waste management system, presence of canals and water bodies that are habitat of pathogens and vectors are highly vulnerable
socio-economic factors
Highly vulnerable are the poor sector of the population. Those that are below poverty income threshold level and have difficult access to health services i.e. cannot afford doctor‘s treatment as well as medicines.
environmental factors
Highly vulnerable are communities close to bodies of stagnant water, unsanitary environment, lack of waste management system, temperature, rainfall and relative humidity favoring the growth of pathogens and vectors.
health/environmental policy
Highly vulnerable are communities and families not covered by policies on the regular monitoring and treatment of diseases and maintenance of a sanitary environment.
The adaptation measures reported by the PHOs and stakeholders in Palawan,
Pangasinan and Rizal are shown in Table 32.
Table 32 Adaptations to Climate Change-related Diseases in Palawan, Pangasinan and
Rizal, 2011
Adaptation Practices
Dengue Malaria Leptospirosis Cholera Typhoid
Individuals and Family
Use of treated or untreated mosquito nets.
Proper use of nets at the right time and right place
Proper use of nets at the right time and right place
Provision of screens and sealing of holes in houses
Prevent entries of mosquitoes
Prevent entries of mosquitoes
Cleanliness of immediate household‘s surroundings
Removal of waters in containers inside and outside the house
Removal of breeding grounds of mosquitoes inside and outside the house by practicing proper waste disposal
Elimination of damp areas conducive for rat‘s habitat. Clean drainage system most often to prevent breeding grounds of rats
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Adaptation Practices
Dengue Malaria Leptospirosis Cholera Typhoid
Water, sanitation and good hygienic practices
Source out water for drinking that are safe or free from contamination. Sterilize water before drinking. Store foods properly avoiding contacts with probable carriers of cholera. Practice sanitation and good hygiene in the family.
Same as cholera
Consciousness on good health maintenance
Early diagnosis and treatment of climate change-related diseases.
Maintenance of pets at home that can reduce growth of vectors and pathogens
Breeding of larvivarous species of fish
Maintenance of cats that feed on rats
Barangay or Community
Presence of active barangay health workers.
Report suspected cases to hospitals for immediate diagnosis and treatment
Microscopists for malaria only for immediate diagnosis and treatment of climate change-health related diseases
Report cases of suspected infected persons for treatment
Refer cases to hospitals for diagnosis and treatment
Refer cases to hospitals for immediate diagnosis and treatment
Decanting Spraying pesticides that are not toxic to human being
Destroy rats and breeding grounds and habitat
Provision of a centralized clean water sources that are well protected and maintained whole year round
Spring development; Prevention of water source contamination by sealing potential entry of pathogens/vectors.
Community ordinances: zoning and resettlement of high risk groups
Resettlement in dengue-free zones.
Resettlement in malaria free zone.
Resettlement in elevated and non-flood prone
Remove sources of water contamination or resettlement contaminated groups.
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Adaptation Practices
Dengue Malaria Leptospirosis Cholera Typhoid
or informal settlers.
areas.
Proper waste management system at community level
Removal of wastes that promote growth of pathogens/vectors; cleaning of water ways
Eliminate breeding grounds of rats
Removal of sources of contamination; location of water sources away from sewage/waste dumping areas. dengue
Prevent sources of pathogens/
vectors coming from waste/sewage areas.
Presence of manned and active health workers in BHCs.
Regular diagnosis, treatment and referrals/endorsement to hospitals that can treat diseases.
Information and Education Campaign at the Community Level
Barangay Health Centers with regular information campaign activities for the barangay population regarding prevention adaptation measures of all diseases
Health systems and infrastructure
Presence of a network of complimentary hospitals complete with laboratory, medicines, and medical facilities within the province where costs of diagnosis and treatments are affordable.
Conduct thorough diagnoses and treatments of infected persons.
Health care system
PhilHealth card necessary for each family
Holistic health maintenance projects
Fourmula-1, Vaccination, PIDSR, etc.
Fourmula-1, PIDSR, malaria treatment medicines, etc.
Fourmula 1, PIDSR, leptospirosis treatment medicines
Fourmula 1, PIDSR, cholera treatment medicines
Fourmula 1, PIDSR, typhoid treatment medicines
Pathogen/vector factors
Innovative practices to eliminate vectors and pathogens.
Solar insecticide capture and destroy
Rats trapping Floating Toilet Device
Floating Toilet Device
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Adaptation Practices
Dengue Malaria Leptospirosis Cholera Typhoid
Regular spraying of chemicals that are non- toxic to human beings to eliminate pathogens and vectors inside and outside the house.
Regular and simultaneous spraying that kills mosquitoes and other insects, fungi and other pathogens in all houses and breeding grounds in a barangay.
Regular and simultaneous decanting by barangay level.
Elimination of growth factors and habitat.
Cleaning of water ways, streams and other water bodies, and proper sanitary practices at the household level.
Cleaning of canals; removal of rat habitat and wastes.
Avoid food spoilage by refrigeration and maintenance of clean and safe water sources.
Socio-economic Factors
Health subsidies in vulnerable communities or barangays.
Subsidies to all vulnerable families in the form of free or affordable health card.
Provision of livelihood and income generating projects to increase income of vulnerable communities.
Planting, processing and marketing of medicinal plants proven to strengthen immune system; manufacture and marketing of decanting and trap gadgets; production and marketing of insect repellants; production, breeding and marketing of pets that feed on insects and rats.
PPP for clean and safe water system
Replace old water system vulnerable to contamination
Replace old water system vulnerable to contamination.
Environmental factors
Forestation Planting and management of integrated forest plantations that drive away mosquitoes
Planting and management of integrated forest plantations that drive away mosquitoes
Planting of forest species that attract rats from residential areas.
Establishment, maintenance and management of sanitary landfills.
Eliminates breeding grounds of insects, pathogens and vectors.
Eliminates breeding grounds of rats.
Eliminates breeding grounds of insects, pathogens and vectors.
Periodic cleaning and declogging of waterways,
Breeding grounds of mosquitoes in stagnant water are destroyed.
Flowing streamflow prevent s
Clean and declogged waterways also washout pathogens and vectors that live
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Adaptation Practices
Dengue Malaria Leptospirosis Cholera Typhoid
streams and rivers to allow waterflow continuously.
depostion of wastes for rat foods .
on stagnant water.
Health/ environmental policy
Policy on the integration of health and climate change education in primary and secondary education
Education on the prevention of dengue at home and in school .
Education on the prevention of malaria at home and in school.
Education on the prevention of leptospirosis
Education on the prevention of cholera
Education on the prevention of typhoid
Climate risk proofing policies
Adoption and implementation of adaptation measures for climate change-related health problems in all DOH projects
Policy on mandatory coverage of population for health care system
Full coverage in highly vulnerable areas.
Policy on Strengthened Provincial Disaster Coordinating Council
Creation of a sub-council on disease-related disaster prevention and management
Disaster preparedness policy.
Nationwide capacity building of people on disaster preparedness brought about by climate change-related diseases.
International, regional, national and local literature on climate change adaptation in
relation to health were gathered as well as reports on combating the four priority diseases
namely malaria, dengue, leptospirosis, and cholera/typhoid. An annotated bibliography
formatted by using endnotes in Microsoft Word which documented all relevant reference
materials on climate change adaptation is in the process of preparation. See book 3 annex
B. Among the collected adaptation measures and disease combating strategies, the team
has identified good and innovative practices that are relevant to national and sub-national
development processes in the health sector.
A short-listing of these adaptation measures was undertaken, guided by a set of
prioritization criteria formulated by the project team. In addition, climate change adaptation
practices that are based on local indigenous knowledge and cultural practices, if there are
any, will likewise were documented.
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2. Field documented Innovative Climate Change Adaptation Options for the
Health Sector
The following section describes three innovative climate change adaptation options
currently deemed effective and implemented in the field validation sites. They are mostly
concerned with water and waste management.
Solar Water Disinfection (SODIS)
Solar Water Disinfection (SODIS) is a simple, environmentally sustainable, low-cost
solution for drinking water treatment at household level for people consuming
microbiologically contaminated raw water. Through the use of solar energy, it
improves the quality of drinking water by destroying microorganisms that cause
water-borne diseases. These pathogenic microorganisms are susceptible to two
effects of sunlight: 1) radiation in the spectrum of UV-A light (wavelength 320-400nm)
and 2) heat (increased water temperature). SODIS takes advantage of the synergy
of these two effects, as their combined effect is much greater than the sum of the
single effects. Thus, the mortality of the microorganisms increases with more
simultaneous exposure to temperature and UV-A light.
SODIS is ideal for disinfection of small quantities of water with low turbidity. Placed
in transparent plastic bottles, contaminated water is exposed to full sunlight for 6
hours to destroy the pathogens. Water with more than 50% cloudiness must be
exposed for 2 consecutive days in order to be safe for consumption. Treatment time
can be reduced to one hour if water temperatures exceed 50°C, and treatment
efficiency can be improved if the plastic bottles are exposed on sunlight-reflecting
surfaces such as aluminum or corrugated iron sheets.
By destroying pathogens present in drinking water, SODIS reduces the occurrence of
enteric diseases such as infectious diarrhea (from bacterial infections with
enteropathogenic Escherichia coli), dysentery (watery diarrhoea from bacterial
infections with Salmonella or Shigella), dysentery (from parasitic infection with
Giardia lamblia (―Giardiasis‖) or Entamoeba hystolytica (―Amoebiasis‖), and cholera
(from bacterial infection with Vibrio cholera).
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Floating Toilet Device
The Floating Toilet Device is a project of the Center for Health Development of
Ilocos, in collaboration with hon. Mayor Alfonso Celeste, officials, and the fish pen
owners association of the municipality of Bolinao. Drinking water contaminated with
fecal matter and vibrio cholera has been strongly associated with numerous cases of
cholera in the 2004 outbreak in Pangasinan. The lack of LGU programs and projects
related to water supply and sanitation led to the re-emergence of the cholera
outbreak in 2008. The lack of investment in such programs and projects has been
identified by the DOH as one of the major environmental factors that contributed to
the persistence of cholera in the province of Pangasinan.
As a long term solution to end the cholera epidemic cycle, the Cholera Containment
Strategy and the environmental sustainability projects under it were employed.
Households living near bodies of water were considered as a major drawback in the
containment strategy. For the purpose of preventing the contamination of water
sources, the technology of floating sanitary toilets was developed and piloted in the
municipality of Bolinao, Pangasinan.
Solar-powered Clean Water System (SCW System)
Water disinfection through the Solar-powered Clean Water (SCW) System,
developed by the Ateneo Innovation Center, involves the use of ultraviolet irradiation
to destroy microorganisms in collected rainwater. The UV lamps are powered by
solar energy, which is collected by solar panels and stored in batteries.
The SCW System is being applied as a method of domestic wastewater treatment in
Calaca, Batangas. For this community with a population of 700, the initial
recommendation is to build 2 sets of 6 water cleaning stations. The following
parameters serve as a basis for this design:
– Houses are still being constructed.
– The rate of effective UV (ultraviolet) irradiation and ceramic filter is at most 2
liters per minute (120 liters in a day).
– The duration of UV operation is 4 hours a day.
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Labor and part devices of an SCW System are estimated at Php 50,000. The Ateneo
Innovation Center can provide training for maintenance of the system. On the other
hand, design and construction of the rainwater tank and collection system will be
done separately by an independent contractor.
3. Prioritized Adaptation Options
Adaptation includes the strategies, policies and measures undertaken now and in the
future to reduce potential adverse health effects. Individuals, communities and regional and
national agencies and organizations will need to adapt to health impacts relating to climate
change. At each level, options range from incremental changes in current activities and
interventions, to translation of interventions from other countries/regions to address changes
in the geographic range of diseases, to development of new interventions to address new
disease threats. The degree of response will depend on factors such as who is expected to
take action; the current burden of climate-sensitive diseases; the effectiveness of current
interventions to protect the population from weather- and climate-related hazards;
projections of where, when and how the burden of disease could change as the climate
changes (including changes in climate variability); the feasibility of implementing additional
cost-effective interventions; other stressors that could increase or decrease resilience to
impacts; and the social, economic and political context within which interventions are
implemented.
Because climate will continue to change for the foreseeable future and because
adaptation to these changes will be an ongoing process, active management of the risks and
benefits of climate change needs to be incorporated into the design, implementation and
evaluation of disease control strategies and policies across the institutions and agencies
responsible for maintaining and improving population health. In addition, understanding the
possible impacts of climate change in other sectors could help decision makers identify
situations where impacts in another sector, such as water or agriculture, could adversely
affect population health.
In reality, many of the possible measures for adapting to climate change lie primarily
outside the direct control of the health sector. They are rooted in areas such as sanitation
and water supply, education, agriculture, trade, tourism, transport, development and
housing. Intersectoral and cross-sectoral adaptation strategies are needed to reduce the
potential health impacts of climate change.
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A policy analysis can determine the feasibility of, and priorities among, these options.
When identifying specific measures to implement, it is often informative to list all potential
measures, without regard to technical feasibility, cost or other limiting criteria; this is the
theoretical range of choice (White, 1961). It is a comprehensive listing of all the measures
that have been used anywhere, new or untried measures, plus other measures that can only
be imagined. The list can be compiled from inventory of current practice and experience,
from a search for measures used in other jurisdictions and in other societies, and from a
brainstorming session with scientists, practitioners and affected stakeholders on measures
that might be options in the future. Listing the full range of potential measures provides
policy makers with a picture of measures that could be implemented to reduce a climate-
related risk, and which choices are constrained because of a lack of information or research,
as a consequence of other policy choices, among others.
Adaptation Evaluation Decision Matrix (AEDM)
AEDM is a decision making tool designed to assist policy makers on what climate change
adaptation measures need to be prioritized and adopted based on a set of criteria. It
answers which of the adaptation measures are worth implementing due their effectiveness in
addressing the disease/ health problems at the soonest possible time at least cost, within
policy mandates and programs, and practicable and implementable at the family level. In
utilizing this tool it shall be assumed that all climate change adaptations that will be included
are cost- effective based on a literature review or country best practices.
The criteria recommended in the AEDM for evaluating adaptation measures are shown in
Table 32. Information required maybe qualitative or quantitative depending on the availability
and type of the information. Qualitative rating can be variations of (+) or (-). Quantitatively,
rates or scores or actual quantitative data such as costs can be used. The criteria are
described as follows:
Practicability of implementation - pertains to feasibility of implementation given the
policies, resources (financial, human resources, equipment/ materials), systems
(financing, monitoring, evaluation, operations and information), guidelines and
organizational structure. The scores or rates or degrees of (+) or (-) will depend on
the presence or absence of these inputs.
Cost- effectiveness – achieving desired health outcomes at least cost. If cost
effectiveness ratios for the adaptation measures are available, these can be used
basis to score or rate low to high cost effectiveness ratios. If no ratios can be
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obtained from information available to the policy maker, then variations of (+) mark
can be used to show degrees of cost- effectiveness (low high). If no data is
available on cost effectiveness, effectiveness measures such as health outcomes
need to be identified first. Examples of health outcomes can are low incidence,
prevalence, or mortality rates related to malaria, dengue, leptospirosis, cholera and
typhoid. Health records in health centers can be used to quantify effectiveness.
Quantitatively, incidence, prevalence or mortality rates can be used. Qualitatively,
variations of (+) and (-) to depict highs and lows can be employed. Costs, on the
other hand, are more straightforward. These are actual total costs incurred by the
health center to prevent diseases such as malaria, dengue, leptospirosis, cholera
and typhoid. If no costs are available, perception of the cost incurred can be
described using degrees of (+) or (-) marks.
Within the policy/ program – this section assumes that there is a policy and/ or
program already in place. The criterion then pertains to the scope of the policy or
program. The extent of coverage or inclusion of an adaptation measure to a current
policy or program will merit variations of (+) mark or a score. Absence of the
adaptation measure in the current policy or program will mean a (-) mark or a score.
Safe to family members – the criterion ensures that the utilization of adaptation
measure will not be hazardous to members of the family or community.
Impact to the environment – pertains to the overall effect to the general environment
of the community.
After the scores or rates have been decided on, totals need to be computed based on (+)
and (-) if done qualitatively or the scores/ rates if done quantitatively.
Table 33 Adaptation Evaluation Decision Matrix
Disease/Adaptation
Measure
Practicability of implementation
Cost-effectiveness
Within policy/program
Safe to family
members
Impact to the
environment
Total
Impact Rating + - + - + - + - + -
Dengue a.Adaptation 1 b.Adaptation 2 Malaria a.Adaptation 1 b.Adaptation 2
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The adaptation measures in Table 34 were obtained from the review of
literature (compendium of good and innovate climate change adaptation practices).
The matrix was completed to demonstrate the way policymakers and implementers
can accomplish it. Adaptation measures are varied and need to be identified given
the context or setting of the community where the measures will be implemented.
As in the earlier discussion on Adaptation Evaluation Decision Matrix, a set of
criteria were used to help policymakers identify cost effective adaptation measures
that are applicable to their local context or setting. Adaptation measures under the
General Adaptation category are cross- cutting interventions and are not solely to
address health issues caused by climate change. Such include harmonization of
policies, health surveillance and early warning, health service delivery and programs,
capacity building, social health insurance coverage, stakeholder partnerships and
capacity building, public awareness on disease prevention and environmental health.
Disease-specific adaptation measures are grouped according to vector-borne
diseases and water/food-borne diseases. For Malaria, Dengue and Leptospirosis,
presence of an integrated vector management program is considered to be a cost
effective measure since it aims to address the management of different types of
vectors found in a particular community. Interventions in the integrated program may
involve decanting and management of wetland breeding sites for mosquitoes and
provision of proper footwear and safe drinking water. For the water/ food- borne
diseases like cholera and typhoid, the best adaptation measures are focused on
proper sanitation, proper human waste disposal and provision of safe drinking water.
The assessment of each of the criterion is discussed on page 124. Based on
the assessment and scoring done, items with 11-15 points are comprised of regular
and existing programs that need to be reinforced by local government units to help
address effects of climate change on health. Items with 7-10 points are measures
that are quite new and are not yet widely implemented, lack current local policy
support, lack implementation mechanisms and/or strong political will to effectively
address health issues. Based on best practices, it is advised that a mix of general
and disease specific adaptation measures be prioritized and pursued. Adaptation
measures can likewise be prioritized and implemented according to a timeframe set
by policymakers and implementers: short-term, medium-term and long-term.
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Table 34 Adaptation Evaluation Decision Matrix based on Literature
Disease/Adaptation Measure
Practicability of implementation
Cost-effectiveness
Within policy/program
Safe to family members
Impact to the environment
Total
Impact Rating + - + - + - + - + -
General Adaptation
Mainstreaming CC in government policies + ++ + + ++ 7
CC Capacity building of policy makers and implementers
++ +++ + - ++ 7
Increased stakeholder partnership and collaboration for CC and disease prevention
++ +++ + +++ + 9
Intensifying public health surveillance and early warning system especially in vulnerable areas
+++ +++ +++ +++ +++ 15
Improvement of health service delivery and clinical and public health programs
+++ +++ +++ +++ ++ 14
Increased social health insurance coverage (safety net)
+++ +++ +++ +++ -- 10
Environmental cleanliness +++ +++ + +++ +++ 13
Reforestation ++ ++ + + +++ 9
Land zoning restrictions and resettlement programs
+ ++ ++ ++ + 8
Reinforcement of public awareness on disease prevention
+++ ++ +++ +++ +++ 14
Dengue, Malaria, Leptospirosis
Integrated vector management program– to include regular decanting; management of wetland breeding sites; proper footwear and provision of safe drinking water
+++ +++ --- +++ +++ 9
Cholera, and Typhoid
Promotion of basic hygiene and sanitation +++ +++ +++ +++ +++ 15
Safe drinking water (SODIS) +++ +++ - +++ + 9
Solar Powered Clean Water system(SCW system)
+++ +++ - +++ + 9
Floating Toilet Device +++ +++ -- +++ +++ 10
+ /- least, ++/--mid, +++/---most
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E. Integrated Monitoring and Evaluation Framework (IM&EF)
The framework in schematic form is shown in Figure 26 (also see Book 2, Appendix
E). The main objective of the IM&EF is to enable periodic M&E of climate change trends and
impacts on the effectiveness of policies and measures for reducing vulnerability and
increasing adaptive capacity in the Health sector. The M&E framework/strategy is based on
existing M&E system in the health sector as well as from M&E systems of NGAs and
international organizations. Consequently, the activities undertaken include the review of
relevant on-going M&E frameworks and analysis of how climate change parameters could
be incorporated in the existing M&E system, as well as the formulation of a strategy on how
to build capacity for implementing the system.
Figure 26 Integrated M&E Framework
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How the framework was formed
The inventory and assessment of relevant on-going M&E frameworks and systems in
the international, national and local levels involved the search for M&E systems related to
climate change from internet sources and from consultation meetings with DOH officials.
At the international level, M&E frameworks were sourced from the United Nations
Development Program‘s (UNDP) Global Environment Facility. These frameworks generally
monitor climate change adaptation and climate change programs. Relative to health, the
International Federation of Red Cross and Red Crescent Societies‘ M&E Sourcebook has
provided information that are useful for the development of climate change adaptation M&E
in the health sector.
At the national level, monitoring and evaluation systems of different government
agencies were examined. These agencies include the Department of Agriculture (DA),
Department of Agrarian Reform (DAR), Department of Environment and Natural Resources
(DENR), Department of Health (DOH), Department of Science and Technology (DOST),
Department of Trade and Industry (DTI), National Economic Development Authority (NEDA),
and the National Statistics Office (NSO).
Relative to health, the National Framework Strategy on Climate Change (NFSCC)
under the Climate Change Act has prioritized the following strategies for the sector:
a. Assessment of the vulnerability of the health sector to climate change.
b. Improvement of climate-sensitivity and increase in responsiveness of public health
systems and service delivery mechanisms to climate change.
c. Establishment of mechanisms to identify, monitor and control diseases brought about
by climate change; and improved surveillance and emergency response to
communicable diseases, especially climate-sensitive water-borne and vector
diseases.
It should be noted that all of the above strategies require the strengthening of
the M&E system, especially on surveillance and emergency response, which should
take into account climate parameters that greatly impact on disease prevalence and
control.
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The Philippine National AIDS Council‘s has developed an M&E system for AIDS, but
the 4th AIDS Medium Term Plan Report has noted difficulty in the system‘s operationalization
due to substantial resource requirements. At the provincial level, a non-health M&E system
was developed for a coastal resource management project in the island province of
Camiguin. The project‘s methodology for fish catch and water quality monitoring and results
analysis provide a useful model for implementing M&E systems at the local level. However,
substantial resource support was noted that could account for the relative success of the
system.
This activity also looked into the determination of the capacity of relevant government
entities at the national and provincial levels including the Department of Health and PAG-
ASA. The different surveillance and monitoring systems of the Department of Health, past
and present, were considered in determining the entry point for the M&E system to be
proposed.
These systems include the following: (1) Philippine Integrated Disease Surveillance
and Response (PIDSR); (2) Monitoring and Evaluation for Equity and Effectiveness (ME3)
through FOURmula One (F1); (3) National Epidemic Sentinel Surveillance System (NESSS);
(4) Field Health Service Information System (FHSIS); and (5) Health Emergency
Management Staff (HEMS). In addition, there is a Notifiable Disease Reporting System
(NDRS) that generates information on 17 diseases and 7 syndromes, the data from which is
used to generate morbidity rates; the Expanded Program on Immunization Surveillance
System (EPI Surveillance) that focuses on the monitoring of priority vaccine-preventable
diseases targeted for eradication and elimination namely: poliomyelitis, measles and
neonatal tetanus; and the HIV AIDS Registry program that keeps track of the number of
AIDS-HIV cases through a voluntary testing program.
With the incorporation of most of the above surveillance systems into PIDSR (as per
DOH Administrative Order 2007-0036 dated October 2007), the latter appears to be the
most appropriate for enhancement through the integration of climate change. PIDSR aims to
address the inefficiencies, redundancies, and duplication of efforts resulting from multiple
surveillance systems, which had resulted in extra costs and training requirements that had
kept some personnel unmotivated and overloaded.
The proposed M&E requires direct coordination with PAGASA and other forecasting
systems, for a more efficient decision-making process. The flow of information to the
government agencies and LGUs would then be supplemented with close coordination with
the DOH Central Office, the provincial offices, its hospitals, down to the rural health units and
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barangay health centers. Adaptation strategies on health that are suited and applicable to
the respective terrain and topography will be left for LGUs to decide upon, after being given
the list of appropriate and validated strategies from Activity 3 of this project.
Validation of the proposed CCA M&E framework and system for the health sector
A framework for the strategy to develop the climate change adaptation M&E system
for the health sector was initially developed. Following the round table discussion with
various stakeholders on April 15, 2010, the project team was advised by NEDA to limit the
M&E system to a form that will enhance existing M&E systems of the national and provincial
development planning processes for the health sector. Efforts to identify entry points to
integrate climate change into the M&E system in the health sector and in the national and
provincial development planning process has zeroed in on PIDSR.
The team has also identified the different principles that will underpin the
development of the M&E Strategy as follows:
It shall verify the effectiveness of the implementation of policies, programs, and
projects in terms of changes and/or improvements in the situation of target groups,
their behavior, application and utilization of skills, and how these changes can be
attributed to interventions such as technical assistance and management services
delivered by implementers.
The M&E system shall build on existing disease surveillance systems to collect
data for the selected diseases and for tracking temperature and rainfall
(precipitation) and should provide a mechanism for integrating, analyzing and
decision-making involving the two data sets.
The M&E system must be simple, provide quick results, be cost-effective and
operated in a manner that is both transparent and with clearly-defined
accountabilities and responsibilities.
The M&E system must be able to provide a mechanism to facilitate the systematic
communication and/or sharing of results across different levels, to include
decision-makers, implementers, and the general public, especially at the level of
the household, as well as for a feedback mechanism.
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The M&E system shall involve a minimum amount of relevant and practical
indicators.
The M&E system shall be subject to review every three years, and may be
modified subject to the lessons learned and to suit the needs of stakeholders.
The detailed implementation of the M&E strategy shall be described under M&E
operations plan, to be formulated and implemented on an annual basis by the
Department of Health.
The team has identified indicators for the M&E system that are relevant for national,
regional and provincial development planning processes in the health sector in relation to
Climate Change. It includes sensitive indicators categorized under several ―dashboard‖
indicators. The matrix is shown as Table 35.
Table 35 Climate Change M&E Dashboard Indicators
AREAS DASHBOARD INDICATORS
FIRST LEVEL
INDICATORS
SECOND LEVEL
INDICATORS
THIRD LEVEL INDICATORS
MONITORING LEVEL
(Barangay, Municipal, Provincial, National)
MONITORING FREQUENCY
(Daily, Weekly, Monthly,
Quarterly, Annually)
POSITION / AGENCY
I. Climate & Env’l Parameters
CLIMATE CHANGE EARLY WARNING
1 % increase beyond rainfall threshold
P D NDCC with PHO
1 degree increase above temperature threshold
P D NDCC with PHO
II. Public Health and Health Service Interventions
EARLY DISASTER RESPONSE
increase in public awareness (say additional 10% of population)
Community participation in disaster preparedness projects & activities
Implementation of adaptation strategies
M M RHU/ BHS
Functional disease surveillance system
Prevalence of diarrhea
Proper waste disposal: ---% of HH with sanitary toilets ---% of HH with clean source of water ---% of HH with proper garbage disposal
M,P W, M SI-RHU PHO
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AREAS DASHBOARD INDICATORS
FIRST LEVEL
INDICATORS
SECOND LEVEL
INDICATORS
THIRD LEVEL INDICATORS
MONITORING LEVEL
(Barangay, Municipal, Provincial, National)
MONITORING FREQUENCY
(Daily, Weekly, Monthly,
Quarterly, Annually)
POSITION / AGENCY
---% of establishments with sanitary permits
Number/% of (+) malarial smears
Active case finding: no. of probable cases
M,P W MHO-RHU PHO
Number/% of validated dengue cases
Active case finding: no. of probable cases
M,P W MHO-RHU PHO
Increased control of infectious disease
Incidence of diarrhea
Proper waste disposal:
% of HH with sanitary toilets
% of HH with clean source of water
% of HH with proper garbage disposal
% of Establishments with sanitary permits
Implementation of programs on integrated vector management
Implementation of decanting activities
M W SI-RHU
ACCESS TO PRIMARY HEALTH CARE
Increased diagnosis of malaria thru microscopy & rapid diagnostic test
Number/% of (+) malarial smears
Active case finding: No. of probable cases
M,P W,M MHO-RHU PHO
Increased treatment of malaria
% of malaria cases treated
Availability of 1st-line and 2nd-line drugs
M,P,N W,M MHO-RHU PHO DOH
Increased diagnosis of dengue cases
% of validated dengue cases
Active case finding: No. of probable cases
M,P W,M MHO-RHU PHO
Increased access to treatment of diarrhea
% of diarrheal cases treated
% of HH with sanitary toilets % of HH with clean source of water % of HH with proper garbage disposal % of Establishments
M,P W,M MHO-RHU PHO
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AREAS DASHBOARD INDICATORS
FIRST LEVEL
INDICATORS
SECOND LEVEL
INDICATORS
THIRD LEVEL INDICATORS
MONITORING LEVEL
(Barangay, Municipal, Provincial, National)
MONITORING FREQUENCY
(Daily, Weekly, Monthly,
Quarterly, Annually)
POSITION / AGENCY
with sanitary permits Nutritional status of under 5 children
III. Environmental and social determinants of health
ENVIRONMENTAL HEALTH AND SAFETY
Integrated vector management
% of Provincial, city and municipal governments engaged in vector management
Existing and functional programs found in LGUs on integrated vector management
B,M,P Q CHO, PHO
Effective Sanitary Programs
% Increase in awareness on sanitary programs
% of HH with sanitary toilets % of HH with clean source of water % of HH with proper garbage disposal % of Establishments with sanitary permits % decrease in food and water borne diseases
B, M, P Q SI- RHU PHO
IV. Health System and Infrastructure
INTEGRATED NATIONAL PROGRAM ON CLIMATE CHANGE
Stakeholder engagement: Existing partnerships of DOH with other CC- related institutions (government and private) Presence of funding for the CC program at the national level
% of health facilities (hospitals, health centers, Barangay stations, clinics) with adaptation
Existing framework and plan Existing working groups on CC
Outputs to collaborative work and projects brought about by existing partnerships
N N
N
Q A
A
USEC- DOH DOH
NEDA
DOH
LGUs
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AREAS DASHBOARD INDICATORS
FIRST LEVEL
INDICATORS
SECOND LEVEL
INDICATORS
THIRD LEVEL INDICATORS
MONITORING LEVEL
(Barangay, Municipal, Provincial, National)
MONITORING FREQUENCY
(Daily, Weekly, Monthly,
Quarterly, Annually)
POSITION / AGENCY
programs and are emergency and disaster preparedness
CC RESEARCH AND DEVELOPMENT
Research and development agenda % of funds/ grants in R&D for CC
Funds raised and utilized for collaborative projects on CC at national and provincial levels
% of research proposals funded
Outputs/ Completion of researches funded
N A
DOH CC related agencies
NEDA
Legend: B – Barangay; M – Municipal; P – Provincial; N – National; D – Daily; W – Weekly; M
– Monthly; Q – Quarterly; A - Annually
Organized into a matrix, the climate change M&E system/s being developed will
include a strategic action plan for mainstreaming the CC M&E into the national and
provincial development planning processes, which will entail the crafting of relevant updated
policies and regulations that take cognizance of the climate change impacts on health.
Previous to the RTD, it was deemed that the M&E framework/system for the health
sector to be workable, must have the following characteristics:
a. It should be user friendly;
b. It should allow participation of multi-agencies working in the health sector
including NGOs;
c. It should allow workability in any levels of governance in the health sector
from the field level to the central office; and
d. It should be cost effective.
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The DOH Central office, with its primary functions for policy and program
development, technical assistance, and training, is expected to lead in mandating the
inclusion of climate change M&E systems into its regional and local offices. This is critical for
mainstreaming the developed M&E framework which will be included in the mandates of the
LGUs and DOH. The system will be included into the policies, programs, and projects of
each locality. As such, financing will also come from the Local Government Units.
PIDSR as the Centerpiece of the Integrated M&E Framework
The framework of the proposed Health Sector Climate Change Monitoring and
Evaluation System is shown in Figure 26. The centerpiece is the conceptual framework for
the Philippine Integrated Disease Surveillance and Response System (PIDSR). PIDSR was
adopted by the Department of Health in 2007 through Department Administrative Order
2007-0036 that was signed on October 1, 2007 by then Health Secretary Francisco T.
Duque III.
The original PIDSR framework was formulated following assessments done in 2006
that showed the inadequacy of having several existing surveillance systems. Prior to the
implementation of PIDSR, there existed four major disease surveillance systems in the
Philippines. These were the following: (a) NDRS, FHSIS or Notifiable Disease Reporting
System of the Field Health Service Information System; (b) NESSS or National
Epidemic Sentinel Surveillance System; (c) EPISurv or Expanded Programme on
Immunization diseases targeted for eradication or, elimination Surveillance System;
and (d) IHBSS or Integrated HIV/AIDS Behavioural and Serologic Surveillance System.
Having multiple systems was found by the 2006 assessment as having contributed to
inefficient surveillance, ―characterized by redundancy and duplication of efforts, extra and
sometimes prohibitive costs, a demoralized health workforce, inaccurate and delayed
reporting, and ultimately unrealized health outcomes‖ (NEC-DOH, 2008).
The other driving force for the integration of disease surveillance systems in the
Philippines was the need for the country to meet its commitment as a member of the
international community, following the adoption on 23 May 2005 of the International Health
Regulations (2005) during the World Health Assembly, where the Philippines is a state party.
IHR 2005 required all state parties ―to carry out an assessment of public health events
arising in their territories‖ and ―then to notify WHO of all qualifying events within 24 hours of
such an assessment‖ (WHO, 2008). Aligning the country‘s disease surveillance and
response system with the requirements of IHR 2005 was needed in order for the Philippines
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to be consistent in its application of the assessment and notification requirements under IHR
2005. Consistency with IHR 2005 was deemed as ―crucial to ensure prompt communication
to WHO of those events which may need coordinated international public health assessment
and response‖ (WHO, 2008). In the light of global threats to public health such as SARS
and avian flu, and given the poor public health infrastructure in the Philippines, the country
could ill afford a disease surveillance and response system that was out of sync with the rest
of the world.
According to the PIDSR Manual of Operations (NEC-WHO, 2008), the PIDSR
framework is one that
“… embodies an integrated functional disease surveillance and
response system institutionalized from the national level down to the
community level. Each level of the health care delivery system interacts
with each other while performing their basic roles and responsibilities.
Standard case definitions to detect priority diseases are to be used in all
disease reporting units and a comprehensive flow of reporting is
adopted.”
ith PIDSR, the health sector has a surveillance system in place that enables early
detection, reporting, investigation, assessment, and prompt response to emerging diseases,
epidemics and other public health threats. By being integrated, PIDSR
―... emphasizes standardized nationwide preparation rather than ad hoc
reactions to outbreaks; it secures human and financial resources
needed to operate an on-going, effective system; monitors disease
outbreaks particularly at the local level; confirms diagnoses if necessary
through laboratory tests; reports outbreaks in a timely manner; responds
with the most effective public health intervention based on hard
evidence; takes action to prevent future outbreaks; and evaluates the
performance of both the intervention and the surveillance system itself.”
Health and relevant institutions that are responsible for taking appropriate action in
response to public health threats have been grouped into two: (a) local and (b) national
disease surveillance and response modules. The local module includes provincial, municipal
and city governments, health offices, hospitals, laboratories, surveillance units, ports and
airports, media, and the community itself. This module is tasked with providing immediate
response in the event of public health threats. The national disease surveillance and
response module consists of corresponding agencies/communities at the regional and
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national levels, with the Department of Health exercising overall leadership and providing
technical support at the national level.
The Epidemiology and Surveillance Units (ESUs) at the municipality, city, and
provincial levels were strengthened and when not yet existing, established to provide public
health surveillance and epidemiology services. Staffing of the local health offices was also
prescribed to ensure the presence of personnel responsible for surveillance. PIDSR-trained
disease surveillance coordinators were also designated by hospitals to be at the frontline of
disease notification, investigation and reporting. At the regional level, RESUs were
responsible for consolidating data from the provinces as well as to provide technical support.
Vital to PIDSR‘s effective functioning is the quality, accuracy and timeliness of
information as well as the existence of clearly-defined reporting and feedback systems for
communicating information. PIDSR has capacitated ESUs at the primary level to transform
data into useful information for front-line management, monitoring and measurement of
progress on local targets. Through PIDSR, information is transformed into evidence, which
when packaged, communicated and disseminated to decision-makers make them ―change
their understanding of the issues and needs‖ (NEC-DOH, 2008). Only when hard evidence is
available would public health action be taken, which insures that interventions made are the
appropriate responses to the threats to public health.
On top of PIDSR‘s framework is its goal of ―Healthier Communities,‖ which is
achieved through the reduction of mortality and morbidity by an institutionalized, functional,
integrated disease surveillance and response system nationwide. The objectives of PIDSR,
as enumerated in the Manual of Procedures (2008), are as follows:
1. To increase the number of LGUs able to perform disease surveillance
and response.
2. To enhance capacities at the national and regional levels to efficiently
and effectively manage and support local capacity development for
disease surveillance and response.
3. To increase utilization of disease surveillance data for decision making,
policy making, program management, planning and evaluation at all
levels.
Features of PIDSR in Relation to Climate Change
In formulating the M&E strategy for the health sector, the scope of work as
prescribed by the National Economic and Development Authority (NEDA) envisioned the
development of a new system and/or the enhancement of existing M&E system for national
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and provincial development planning processes. The team opted for the latter, and selected
to anchor the M&E system for climate change in the health sector on PIDSR.
PIDSR – its advantages for climate change M&E
The team took notice of the following advantages of PIDSR which firmed up the decision to
anchor the M&E for climate change in the health sector on this existing system:
1) Inclusion of the five notifiable diseases, namely malaria, dengue, cholera,
typhoid and leptospirosis, as targets for surveillance, under the category
―epidemic prone diseases.‖ With the project team‘s decision to focus on the
five notifiable diseases, it was deemed important to use an M&E system that
keeps track of these highly infectious, climate-sensitive diseases.
Providentially, PIDSR has included them as targets for surveillance; thus,
PIDSR affords an avenue in which the proposed monitoring system on the
impacts of climate change on the five selected diseases can easily be
configured.
2) PIDSR lends itself to early detection and response to epidemics. In view of
the anticipated climate change impacts on health, which would assume
epidemic proportions under business-as-usual scenarios, the capacity of
PIDSR for early detection and response would be harnessed to address
abnormally frequent disease occurrences and other emergency health
situations borne by climate change.
3) PIDSR strengthened local capacity for surveillance and response.
Vulnerabilities to climate change encompass the entire spectrum of society,
but the degree varies depending on a number of factors. Vulnerabilities at the
local levels, especially in resource-poor communities, are most likely to be
high. This necessitates the ability to make localized decisions based on
relevant and timely information and to intervene when necessary to avert
projected disaster situations. With increasing human health risks attributable
to climate change, the importance of having a local-level M&E system that is
linked to the national level system for immediate support, cannot be over-
emphasized.
4) Integrated response to epidemics and other public health threats. The
impacts of climate change on health will most likely be brought about by the
interplay of factors that will influence the severity and scope of disease
occurrences and the lingering effects of disasters. Under such conditions,
piecemeal approaches to respond to public health threats would produce
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negligible and short-lived results. Having an integrated system may afford
holistic solutions that will be more effective in the long term.
5) Open lines of communication are established at all levels. Actions at all
levels, from the disease reporting units up to the national planners and
decision-makers, that are designed to respond to climate change, will
invariably require timely and reliable information from many sources. An
institutionalized communication system that allows free exchange of
information is critical to the success of interventions designed to reduce
vulnerabilities to climate change, particularly during extreme weather events
and occurrences of climate-related disasters.
6) PIDSR uses the latest in information technology for the reporting and
dissemination of information. This augurs well for quick data consolidation,
analysis and interpretation which are necessary inputs for prompt disaster
response and real-time decision-making. Correlation with climatic data is also
made a lot easier.
Limitations of PIDSR in relation to climate change
Despite its advantages, PIDSR in its present form cannot yet be fully adopted without
modification due to some inherent limitations. The following are some of the features which
are specific to the diseases that may limit PIDSR‘s usefulness as an M&E system on climate
change in relation to health:
1. Dengue and malaria:
All indicators used to monitor dengue and malaria are medical signs and symptoms.
No environmental data are collected to predict and confirm dengue and malaria cases.
2. Leptospirosis:
The indicators used to monitor leptospirosis cases include medical signs and
symptoms as well as exposure to infected animals or an environment contaminated with
animal urine (e.g., wading in flood waters, rice fields, and drainage). The climate change-
related indicator used is only precipitation. Temperature and relative humidity are not
considered.
3. Cholera:
The indicators used to monitor cholera are cases of dehydration and acute watery
diarrhea to track the history of cholera epidemicity in an area. No environment or climate
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change-related indicators are being monitored to be used as tool in predicting and/or
anticipating suspected cholera cases.
Proposed Modifications to PIDSR to make it Climate Change Compliant
As mentioned in the foregoing discussions, the proposed M&E framework is anchored on an
existing M&E framework in the Health sector which is PIDSR. However, as can be seen in
Error! Reference source not found., the project team is proposing that PIDSR be modified to
reflect the importance of periodic assessments of vulnerabilities and the monitoring of
adaptation systems in the light of climate change. It is strategic for the proposed M&E
framework to be linked to the frameworks for these activities as well.
A common understanding of the meaning of vulnerability and adaptation is important. Hence,
the following definitions of these and other related terms have been adopted, both from the
IPCC report and the Climate Change Act of 2009 (GEF, 2008; Climate Change Act of 2009):
Vulnerability is the degree to which a system is susceptible to, or unable to cope with,
adverse effects of climate change, including variability and extremes. It is a function of the
character, magnitude, and rate of climate change and variation to which a system is
exposed, its sensitivity, and its adaptive capacity.
Adaptive capacity is the ability of a system to adjust to climate change (including climate
variability and extremes) to moderate potential damages, to take advantage of opportunities,
or to cope with the consequences.
Adaptation refers to adjustments in natural or human systems in response to actual or
expected climatic stimuli or their effects that moderates harm or exploits beneficial
opportunities.
As per IPCC, various kinds of adaptation can be distinguished as follows:
Anticipatory adaptation – Adaptation that takes place before impacts of climate
change are observed. This is also referred to as proactive adaptation.
Autonomous adaptation – Adaptation that does not constitute a conscious response
to climatic stimuli but is triggered by ecological changes in natural systems and by
market or welfare changes in human systems. This is also referred to as spontaneous
adaptation.
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Planned adaptation – Adaptation that is the result of a deliberate policy decision,
based on awareness that conditions have changed or are about to change and that
action is required to return to, maintain, or achieve a desired state.
GEF (2008) further qualified that climate change impact or vulnerability assessment is ex-
ante evaluation which should be distinguished from monitoring and evaluation of adaptation
interventions which is ex-post evaluation in nature.
Vulnerability assessment and its link to the M&E framework
On the left hand panel of the proposed M&E framework is the abridged vulnerability
framework (described in great detail in other parts of the report). It is sufficient to mention the
following main points on the methodology used and the significance of the framework vis a
vis the overall M&E for climate change. As mentioned in the foregoing section, assessing
vulnerabilities is an ex ante analysis, which for the health sector means being able to
measure susceptibility to diseases, determining potential causes that contribute to more
prevalent disease spread, and identifying weaknesses in the health system that can further
deteriorate due to climate change.
The starting point for defining vulnerability relative to the five climate-sensitive
diseases was the epidemiological triad which consists of host, pathogen and environment,
and what contributes to these factors.
Relative to host as a factor, the contributors to this component of the triad have been
broken down to individual and family and/or community related factors. Individual
vulnerabilities can be determined from the following: (a) standard of living, (b) disease
reservoir or the current level of infection, (c) genetic make-up, and (c) personal habits.
Determinants of family community vulnerability include: (a) population density and growth,
(b) unemployment and poverty levels, as well as (c) migration patterns and degree of
urbanization.
On the aspect of pathogen as a factor, the following are deemed as contributory
causes: (a) microbe replication and movement, (b) vector reproduction and movement, (c)
microbe and vector evolution, (d) feeding frequency and longevity, and (e) habitat formation.
Vulnerabilities arising from the environment may be derived from contributions from
the following: (a) the state of watersheds and forest cover, (b) loss of biodiversity, (c) access
to safe water, (d) occurrence of flash floods, and (e) other factors such as sanitation, solid
waste management, agricultural production and government policies and regulations that
impact on human settlements, land use and zoning.
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Finally, it is also important to know how the health system (including health
infrastructure, quality, and access to health care) and health policy and regulations are
making an impact on the vulnerabilities of individuals and communities relative to the five
diseases.
Apart from the disease-related vulnerabilities that emanate from the epidemiological
triad associated with each of the five (5) climate-sensitive diseases, the country‘s
vulnerability associated with the occurrence of tropical cyclone is ranked highest in the
world, and third in terms of people exposed to such seasonal event (CCC, 2010). On
average, twenty (20) typhoons hit the country each year. El Niño droughts and La Niña
flooding have been triggered by extreme climate variability. Erosible soils along
steep/unstable mountain slopes, degraded forests and watersheds, unplanned settlements,
combine with geologic/seismic dangers to put communities and individuals more prone to
climate-related disaster risks.
It will be instructive to mention that the Vulnerability and Adaptation (V&A)
Assessment Toolkit published by the Philippine Rural Reconstruction Movement under the
Second National Communication on Climate Change (2009), identified the following
communities as vulnerable to the effects of climate change on health: (a) far-flung barangays
(mountainous or coastal); (b) populations that have least access to health services and are
in congested/dense urban slum areas; (c) those in areas that are endemic to climate-
sensitive diseases, e.g., malaria, coupled with ―bad‖ health system; and (d) those that are
culturally challenging, i.e., resistant to health education or change in their behavior towards
health, brought about by culture or beliefs.
Adaptation strategies and relationship with the M&E framework
On the right hand panel of the M&E framework can be seen the abridged framework
for the proposed adaptation strategies for the health sector. It should be readily apparent
that the different categories of proposed adaptation strategies correspond with the identified
vulnerabilities in the health sector relative to climate change. The evaluation of adaptation
interventions takes the form of an ex post analysis, which means that what is going to be
measured is the effectiveness of the proposed strategies in bringing about an improved
capacity of individual and communities to weather the effects of climate change. It should be
evident that strategies that result in improved adaptive capacity actually reduce
vulnerabilities in the long term.
Although presented in separate boxes in the framework, the assessment of
vulnerability and the formulation of adaptation strategies are best undertaken following a
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continuum that enables the use of the results of vulnerability assessments as inputs for
decisions vis a vis adaptation practices. This relationship is shown in the following matrix
table which outlines the steps in V&A assessment in the health sector using climate sensitive
diseases as the identified vulnerability (PRRM, 2009):
Table 36 Matrix showing the Climate Change and Health V&A Assessment Flowchart
Climate Change and Health V&A Assessment Flowchart
Step 1 Identify/Screen
health vulnerability in area/community
Step 2 Conduct analysis
(Quantitative/Qualitative)
Step 3 Identify action to be
taken
Step 4 Evaluate and
feedback
■ Presence of diseases (determine climate sensitivity/consider epidemic potential)
►Consider number
of cases, occurrence of disease
■ Utilize sentinel sites NESSS/MET for weather parameters
■ Focused group discussions/KII
■ Preventive (adaptation) over curative (mitigation) parameters
■ Prioritize measures ○ Efficiency vs.
Effectiveness ○ Cost/timeframe
►i.e., information drives/mass screening, smearing for febrile people, fast lane for Dengue
■ Policy formulation for health impacts – climate change compliance/resilience
■ Utilize statistical analysis and correlate adaptation measure
■ Identify indicators of success (intermediate and long-term)
■ Refine flowchart to incorporate other factors (i.e., socio-economic)
■ Availability of response mechanisms
► Health infrastructure (human and financial/infra – health centers/hospitals
■ Occurrence of extreme weather events (quantity and quality)
Note: adapted from: Philippine Rural Reconstruction Movement (PRRM). 2009. Vulnerability and Adaptation Assessment Toolkit. Philippines Second National Communication on Climate Change
Principles of climate change M&E for the health sector
The team has identified the different principles that underpin the development of the M&E
Strategy as follows:
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• It shall verify the effectiveness of the implementation of policies, programs, and projects
in terms of changes and/or improvements in the situation of target groups, their behavior,
application and utilization of skills, and how these changes can be attributed to interventions
such as technical assistance and management services delivered by implementers.
• The M&E system shall build on existing disease surveillance systems to collect data
for the selected diseases and for tracking temperature and rainfall (precipitation) and
should provide a mechanism for integrating, analyzing and decision-making involving
the two data sets.
• The M&E system must be simple, provide quick results, be cost-effective and
operated in a manner that is both transparent and with clearly-defined
accountabilities and responsibilities.
• The M&E system must be able to provide a mechanism to facilitate the systematic
communication and/or sharing of results across different levels, to include decision-
makers, implementers, and the general public, especially at the level of the
household, as well as for a feedback mechanism.
• The M&E system shall involve a minimum amount of relevant and practical
indicators.
• The M&E system shall be subject to review every three years, and may be modified
to take into account the lessons learned and to make it more attuned to the needs of
stakeholders.
• The detailed implementation of the M&E strategy shall be described under M&E
operations plan, to be formulated and implemented on an annual basis by the
Department of Health.
This proposed set of principles is summarized in matrix format, as shown in Table 37, for the
purpose of assigning responsibility centers, identifying appropriate tools, and listing of
relevant indicators that will insure that each of these principles are observed/met in the
implementation of the M&E framework.
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Table 37 Matrix table showing the monitoring and evaluation principles, responsible actors and levels, relevant tools and indicators to be collected
Monitoring and Evaluation
Principles Actors / Levels
Tools Indicators Collected Indicators to be
added
A. Tracking of Implementation of Policy, Programs and Projects
Policy: DOH Field level effectiveness: RHUs, local governments
PIDSR aggregate report incorporated into the Field Health Service Information System (FHSIS) annual morbidity report
Health status statistics, health services coverage, notifiable diseases
Environment / climate indicators
B. Build on Existing Surveillance System by Integrating Environmental Data Collection
Enabling policy: DOH & DOST Data analysis: National, regional and provincial Health Offices
Yearly report showing correlations between notifiable diseases and environmental factors
Same as above Correlation statistics
Capacity building needs on data analysis at different levels
C. Promote transparency and demonstrate clearly-defined accountabilities and responsibilities.
Enabling policy: DOH Field level application: Regional, Provincial, City and Municipal levels
Yearly summaries that can be readily attributed to weekly, monthly, and quarterly reports
Health systems statistics
PAGASA providing data services on climate parameters to health system operators
D. Systematic communication and/or sharing of results across different levels.
ALL levels Reports, bulletins, press releases, radio broadcasts
Capacity building strategies for personnel of DRUs/ESUs; Availability of communication systems
Contact details of relevant media outlets and of health offices in various levels
E. Prudent selection of relevant and practical indicators
DOH, NDCC and Related Agencies
DAOs and other related policy issuances
Measures of usefulness of indicators
F. Systematic review and development of the M&E system every 3 years
Enabling policy: DOH Inputs for system modification: All levels
M&E framework for the health sector
Results of periodic assessments
G. M&E operations plan formulation and implementation
DOH: for integration into overall DOH annual operations plan
DOH annual operations plan
Budget for M&E
The above principles complement the guiding principles of PIDSR as contained in DAO
2007-0036. The M&E framework on climate change for the health sector upholds the
leadership of the DOH and is supportive of the agency‘s goal of attaining a more responsive
health system for the country. The proposed M&E principles are also in agreement with the
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spirit of decentralization with the strengthened recognition of the roles of local governments
on matters pertaining to health. It also aims to be compatible with the 2005 IHR surveillance
and response standards. It is designed to be useful and flexible, adopts the principle of
partnership and shared responsibility, maintains privacy and confidentiality of patients‘
information, and will continue to demand professionalism and high ethical standards among
public health workers.
Information needs and M&E strategy at different levels
National level
Information needs and uses
• The Department of Health will benefit from information on how the impact of climate
change on the five (5) climate-sensitive diseases is influenced by policies and programs
that are planned, designed and coordinated at the national level. In turn, the information
will serve as inputs to modify, adjust or enhance government policies and programs, as
well as those of partner institutions. Likewise, information from M&E will be useful in
allocating DOH‘s resources and in directing potential partners to areas where support
and assistance are needed. M&E information will also result in a better understanding of
the factors that contribute to success or failure of interventions, and in ensuring the
sustainability of achieving desired results for an extended time period. Finally, M&E will
provide information on capacity strengthening requirements at all levels of
implementation, which the national level agency (DOH) can use in negotiating with
global donors.
M&E strategy
• The Department of Health (DOH) shall perform leadership and coordination roles in the
implementation of the M&E strategy. It shall establish and maintain its linkage with the
Department of Science of Technology (DOST), through the Philippine Atmospheric,
Geophysical and Astronomical Services Administration (PAGASA), which ―provides
flood and typhoon warnings, public weather forecasts and advisories, and other
specialized information and services primarily for the protection of life and property and
in support of economic, productivity and sustainable development.‖ DOH shall also
coordinate with the National Disaster Coordinating Council (NDCC), being the
President‘s adviser on disaster preparedness programs, disaster operations and
rehabilitation efforts undertaken by the government and the private sector. NDCC acts
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as the top coordinator of all disaster management efforts in the country. It also serves
as the highest allocator of resources to support local Disaster Coordinating Council
(DCC) efforts.
Local levels
Information Needs and Uses
Local government units (LGUs) and district/provincial hospitals will benefit from
information that will enable them to assess impact of climate on disease occurrence at
the local level, and to measure effectiveness of adaptation measures as well as to make
decisions which to employ given local situations. In the case of impending climate
extremes such as flooding, LGUs should be able to immediately provide localized
responses and harness local partners to undertake the issuance of early warning
systems, direct rescue operations, mobilize resources for containment of vectors, and to
track movement of people affected by diseases so as to limit their spread and reach.
M&E information shall enable LGUs to identify the weaknesses of data gathering
systems, data quality and reliability that will be used as the basis for M&E system
improvement, and of human resources (individual households, community members
and health service providers) and institutional capacity building needs.
M&E strategy
M&E should build on existing systems, and should be based on available local
resources and capacity, but enhanced through the setting up of coordination
mechanisms with local weather stations, local radio stations and other mass media
communication systems, and with clearly defined roles and timeframes in terms of data
analysis, decision-making, and reporting mechanisms. The M&E system shall empower
households and communities to implement measures for disease prevention and control
and/or to minimize effects of extreme climate conditions when given clear and timely
warning signals from LGUs. It should be linked with local disaster coordinating councils
(at the level of the provinces, cities and municipalities, and barangays) on disaster
preparedness and mitigation efforts.
Indicators for the proposed M&E framework
Indicators are vital in measuring the effectiveness of climate change adaptation
interventions. Dashboard indicators are the ultimate measures of four domains in the
Monitoring and Evaluation matrix (Climate and Environmental Parameters, Public Health and
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Health Service Interventions, Environmental and Social Determinants of Health, and Health
System and Infrastructure). Contributing to the attainment of dashboard indicators are the
1st, 2nd and 3rd level indicators. Based on the matrix, most of the indicators measured at the
level of the community are 1st level indicators. The matrix is composed of eight sections
and these include:
Domains
Dashboard indicators
3rd level indicators or outcome indictors
2nd level indicators or output indicators
1st level indicators or objective indicators
Level and frequency of monitoring
Position or agency responsible
Source of information
The M&E matrix/ table contains different indicators for various stakeholders such as
PAGASA, DOH and provincial and municipal LGUs as indicated in the ―position or agency
responsible‖ part of the matrix. First level indicators are the most basic of the 4 indicators
and are first accomplished before 2nd level indicators can be attained. Second level
indicators, on the other hand, contribute to the attainment of 3rd level indicators. Third
level indicators also contribute to the description and attainment of the Dashboard
indicators. Dashboard indicators are the final measures of overall attainment of the M&E
domains.
In the M&E matrix, Indicators are read from right to left of the matrix or from 1st level
indicators to dashboard indicators. Note that some of the indicators should be 1) routinely
collected and such should be adopted and institutionalized by responsible agencies and 2)
there are those that are collected once. Specifically, the Planning Team needs to:
1. Commit to the monitoring and evaluation of indicators. Commitment may
be done through an ordinance or resolution or administrative order.
2. Review and identify M&E areas or domains based on the matrix. The
team must be able to assess which of the M&E domains have already been
established or have some accomplishments and which are relatively new.
3. Identify persons responsible for the monitoring and evaluation.
Because the domains and indicators are many, it is proposed that a team be
created to comprise people who will be responsible for implementing the
M&E. While there will be persons collecting information at the level of the
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community such as those doing the work and are familiar with the M&E
indicator and how the information for these indicators will be collected, a
team responsible for overseeing the implementation of the M&E is essential.
4. With the use of the CC ME matrix, identify the indicators that will apply
or that you will need to collect to your level.
5. To adopt the ME matrix, identify indicators in the matrix that is already
routinely collected at your level.
6. Because there is a hierarchy of indicators, adopt and institutionalize those
that you still do not have and classify them according to whether they will be
routinely collected or not. To accomplish nos. 4-6, it will be helpful to use a
table or checklist in summarizing the information.
CC M/E
Domains
and
Indicators
With
data
Without
data
Method of
collection for
indicators
without data
that is
regularly
collected
Frequency
of
collection
(daily,
monthly,
quarterly, bi-
annually and
annually)
Means of
data
collection
Persons or
sub-teams
responsible
for
collecting
data
Partner
agencies for
data
collection
(government
or private
sector
groups)
1. Identify mechanisms by which information/ data can be collected (see last
column of the table). Indicators may measure specific change in behavior or
attainment of specific activities or interventions. Examples include:
o Routine survey of sampled households in the community (HH are
sampled especially if it concerns the whole province or a group of
provinces). Examples of indicator data that can be collected through this
method include (see matrix):
Compliance to program IECs e.g. 4 o‘clock habit (dengue); water
disinfection and hand-washing (cholera and typhoid); Use of
treated bed nets (malaria)
% of HH with sanitary toilets
% of HH with proper garbage disposal
% of HH with SAFE source of water
% of Establishments with sanitary permits
# of live larvae found in selected households (Breteau index)
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o Routine survey of community environment through the use of an
observation checklist. Examples of data that can be collected through this
method include (see matrix):
Low lying areas; Presence of areas with stagnant/ swampy water
in the municipality/ city
Flooded areas in the community
o Review of routinely collected information at the Rural Health Unit or
Health Center and identification indicators can be used for climate
change. Examples of indicator data that can be collected through this
method include (see matrix):
#/% confirmed dengue cases
#/% confirmed leptospirosis
# of probable cases of malaria
# of probable dengue cases
# of deaths due to malaria disaggregated by age and sex
Availability of 1st-line and 2nd-line drugs for malaria, leptospirosis,
cholera, dengue
% of Fully Immunized Children (FIC)
% exclusively breastfed children until 6 months
o Collection of records or documents at the LGU, DOH and DOST-PAGASA
levels that specify that the indicator has been achieved. Such may be IEC
(information, education and communication) materials, memorandum of
understanding or agreement, written guidelines, ordinances/ resolutions
or other forms of policy instruments, plans, project reports, budgets and
meeting minutes. Examples data that can be collected through this
method include (see matrix):
MOA among PAGASA/ local synaptic station, DOH and NDCC/
PDCC/MDCC
Trained PAGASA and LGU HR on local data analysis
Timely weather related information/ trends/ warnings shared by
PAGASA to DOH and NDCC and LGUs through PDCC
Daily weather forecast information provided by PAGASA and
shared with DOH and NDCC and LGUs through PDCC
PAGASA data used by LGU for planning for early response
# of capacity building seminars at LGU level on health
emergencies and early response and adaptation
151
% of LGU projects and activities funded and implemented on early
response and adaptation, health emergency preparedness
projects and activities
Presence of an adaptation plan/ strategy at the level of the
barangay
PIDSR data analyzed with PAGASA data
Trained NEC- DOH and PAGASA HR to analyze and interpret
data
DOH Surveillance teams launched to investigate outbreak or
health emergency situations
Appropriate LGU response to outbreak or health emergency
DOH policy on integrated vector management enacted
Existing and functional programs found in LGUs on integrated
vector management
National and local CC framework and implementation plan
# of R&D CC project proposals submitted by LGUs and institutions
to DOH
Congressional bill (PNHRS) for CC and health research funding
passed; Continuous sourcing out funds from international
community
2. For each, remember to determine the regularity of data collection per
indicator and the persons responsible. Persons or sub-teams responsible for
collecting data are very important.
3. While it is important to identify M/E indicators at your level, it is also
essential to identify data will that other agencies will contribute to your
indicators. Coordination and collaboration with these agencies are necessary.
There is also a need to identify data that will be collected with the help of other
agencies in government or in the private sector.
4. Determine the need for new data collection tools and design them. For new
data that will need to be collected. It is recommended that a technical working
group be formed to work on the data collection tools or forms.
5. Determine the process of reporting through the DOH PISDR.
6. Determine human resource capacity/ capability in collecting data, recording
and reporting. CC capacity building training may be necessary to develop skills
in collecting, recording and reporting new information/ data. Training proposals
for funding can be developed to address capacity gaps.
152
Reconfiguring the existing reporting system for PIDSR
Current reporting arrangements/flow of information and responsibilities at various levels
As per PIDSR Manual of Procedures (2007), the existing reporting system starts from the
local/municipal/city level and goes up to the national level through a number of steps that
involves the provincial and regional levels. Each level and the units involved have clearly-
defined and specified functions, and the flow of information between and among these
bodies is shown in Figure 27.
153
Modified PIDSR Reporting and Response System Flow Chart w/ Climate Change
Municipal or City Gov’t.
Community Barangay
Gov’t & Priv.Hospitals
Gov’t & Priv. Laboratories
Ports & Airports
RHUs
CHOsPHOs
CHDs
NECHEMsOther
gov‘t
agencies
NCDPC
Disease reporting units
-Submit reports of
cases
- Local governments
evaluate site-specific
adaptation strategies
with Health service
providers
Provincial Gov’t
- Provide disaster
& calamity funds
-Collect, organize,
analyze & interpret
surveillance data
- Assess reported
epidemics
- Submit report to higher
bodies
- Evaluate provincial
climate change
adaptation strategies
with provincial LGU
-Assess reported
epidemics
- Assist in local epidemics
investigation & control
- Submit weekly notifiable
disease SR
- Evaluate regional
climate change
adaptation strategies
-Update technical
assistance &
recommendation
- Recognition, prevention &
control of diseases
-Assess all reported epidemics
- Provide direct operational link
- Implement integrated national
epidemic & preparedness plan
- Facilitate budget allocation for
surveillance & response
-Support thru specialized staff &
logistical assistance during
epidemic investigation &
response
- Evaluate national Climate
Change adaptation strategies
-Act as coordinating unit &
operations center for all
health emergencies &
disasters & incidents with
potential of becoming an
emergency
- Adjust emergency
responses based on
climate-related
information
Local Weather
Stations
- Provide weather
data during
extreme weather
events
PAGASA
- Provide
updated climate
data
Health and climate change impact
models
(It is assumed that organizations/units at all levels
implement site-specific adaptation strategies)
Figure 27 Proposed modification of PIDSR reporting system in the light of Climate
Change
154
IV. CONCLUSIONS
A Vulnerability and Adaptation Impact Assessment Framework for the health sector
was devised for this project and grounded the results and outputs of the study. The research
team utilized this framework to test the other deliverables of the study and found that the
framework works.
The categories of vulnerabilities that were culled from the review of literature and the
round table discussion provided focus and specificity to the vulnerability assessment and
adaptation documentation. Hence the team deems it is safe to be recommended for use in
the country. While utilizable, the team avers that the framework can still be refined through
pilot tests of the framework and its parts in different areas of the country and for various
usages.
The following section provides more specific conclusions from the applications of the
vulnerability assessment models that were derived in the project.
1. Disease impact models for dengue, malaria, and cholera were developed out of available
data from the NCR and PHOs of Palawan, Pangasinan and Rizal. The robustness of the
models depends on the accuracy of the health and climate data measurements or
estimations. Leptospirosis and typhoid impact models were not formulated due to
inadequate data. Disease impacts for 2020 and 2050 were conducted using the
projection models that passed the statistical screening process. Refinements of the
models may be done as additional data on health and climate change are made
available.
2. Assessment and evaluation of health data showed purely medical-related data and no
climate change data. This was a major problem. A remedial measure adopted was to
match climate change data from PAGASA in the NCR, Palawan, Pangasinan and Rizal.
The health data were also found incomplete in leptospirosis and typhoid. Also, disease
cases were not available in Palawan, Pangasinan and Rizal. This could be due to lack of
real disease occurrence or with disease occurrence but no documentation. Likewise, in
NCR, only disease cases were available.
The Time Series Analysis also provided important insights into the climate change
and impact assessments:
155
3. Consistent results that the observed minimum temperatures for the current month
provide the most significant positive contributions to the model to predict the number of
dengue cases for any month of observation.
4. A peak of dengue incidence occurs thereafter in about a month after the start of
the increase of cases. This coincides with the years when a surge of cases was
experienced in NCR (as was mentioned previously: 1996, 1998, 2001, 2005 and
2006). (This is evident as crests of maximum temperature seem to frequently
transpire a little earlier compared to the peaks of minimum temperature. This
would be consistent with the lack of significance in estimates for dengue cases
based on maximum temperature.)
Economic impact analyses were accomplished for dengue and malaria. Other
diseases such as leptospirosis, cholera, and typhoid were not covered in the economic
analysis due to lack of data. Rizal was not also covered because of lack of both climate
change and economic data on the selected diseases.
5. The results in NCR and Palawan indicate that malaria and dengue in 2020 would require
about 1% of its annual income for the diagnoses and treatments of malaria and dengue.
In 2050, the allocation for funding for the same diseases would reach no less than 2% of
the provincial income.
6. However, Pangasinan in 2020 would need about 18% of its income only for diagnoses
and treatments of malaria and dengue. In 2050, the budget requirement for both
diseases would be reduced to 4% owing to the reduced number of malaria and dengue
cases, which is not attributed to preventive measures that would be implemented, but to
the changes in the climate indicators. Such climate change nonetheless, may be good
from the point of view of reducing disease occurrence.
7. Considering the five diseases for budgeting purposes, the provincial government may
allocate in the future roughly a total of 2.5% of the income of Palawan in 2020 and 5% in
2050 assuming that the average cost requirement of each disease would more or less be
the same with malaria and or dengue. On the other hand, Pangasinan would allocate
roughly 45% of its income in 2020 to address the five diseases and 20% in 2050.
8. Considering the substantial savings that could be generated from applying preventive
measures, the two provincial governments may consider investing on financing
156
preventive measures to lessen the cost impacts of the diseases, thus lessen the burden
of the provincial governments in addressing these diseases.
9. Provincial and municipal governments should not wait for the diseases to reach epidemic
levels before they address the malaria and dengue outbreaks as well as other diseases
that would emerge and be aggravated by climate change conditions. It is most certainly
beneficial to prevent disease outbreaks before they even emerge.
10. Applying effective preventive measures against dengue would result in significant
savings on the part of the provincial government in the amounts of PhP 10.9 M in 2020
and PhP 31.69 M in 2050.
157
V. RECOMMENDATIONS
1. The most critical recommendation that this study provides is that there is a need to
improve the data bases and information systems that feeds into climate change
vulnerability and adaptation assessment for the health sector. Currently, there are no
linked data between health outcomes and meteorological information. Timely disease
surveillance and case finding may be triggered by accurate weather and climate
information that should be provided to health and LGU managers at all levels.
Governments should engage more actively with the scientific community, who in
turn must be supported to provide easily accessible climate risk information.
Climate risk information should put current and future climate in the perspective of
national development priorities.
Information needs of different actors should be considered and communication
tailored more specifically to users, including the development community
2. Another major recommendation is to create systems to strengthen mainstreaming
adaptation within existing poverty alleviation policy frameworks. There is a lack of
research on the extent to which climate change, and environmental issues more broadly,
have been integrated within national policy and planning frameworks. National
Adaptation Programmes of Action or NAPA was a project funded by the Least
Development Countries Fund (LDC Fund) and commissioned by the UNFCCC to the 48
least developed countries need to be utilized for this purpose.
This is critical. Examples of efforts from Sri Lanka, Bangladesh, Tanzania, Uganda,
Sudan, Mexico and Kenya are presented, highlighting a number of key issues relating to
current experiences of integrating climate change into poverty reduction efforts (IDS, 2006).
As previously discussed climate change stakeholders at all levels are increasingly
engaging with the question of how to tackle the impacts of climate change on development
in poorer nations. There are growing efforts to reduce negative impacts and seize
opportunities by integrating climate change adaptation into development planning,
programmes and budgeting, a process known as mainstreaming. Such a coordinated,
integrated approach to adaptation is imperative in order to deal with the scale and urgency of
dealing with climate change impacts (IDS, 2006).
158
In developed countries progress on mainstreaming climate adaptation has been
limited. Many countries have carried out climate change projections and impact
assessments, but few have started consultation processes to look at adaptation options and
identify policy responses.
Experiences so far highlight a number of barriers and opportunities to mainstreaming
climate change adaptation in developing countries. These are focused around information,
institutions, inclusion, incentives and international finance, and result in a number of
recommendations for national governments and donors.
In the context of climate change, mainstreaming implies that awareness of climate
impacts and associated measures to address these impacts, are integrated into the existing
and future policies and plans of developing countries, as well as multilateral institutions,
donor agencies, and NGOs.
Developing countries, despite having contributed least to greenhouse gas emissions,
are likely to be the most affected by climate change because they lack the institutional,
economic, and financial capacity to cope with the multiple impacts.
Poorer developing countries are at risk as they are more reliant on agriculture, more
vulnerable to coastal and water resource change, and have less financial, technical, and
institutional capacity to adapt.
Under the United Nations Framework Convention on Climate Change, countries have
to report on steps they are taking to address climate change (mitigation) and its adverse
impacts (adaptation). Submitted reports are called National Communications and the
chapters on adaptation contain information on baseline conditions and their linkages, which
might include climate-related disaster effects and response capabilities, population, food
security and agriculture, climate and health, environmental problems, and financial services
available for management of climate risks.
The following section provides more detailed recommendations for specific research
outputs:
On Impact modeling
For better disease impact modeling, the following are strongly recommended:
1. Improve existing database on health and climate change through standardization of
health and climate change data monitoring forms and that data gathering should be
localized. Since the occurrences of diseases are localized and climate change variations
159
are also localized, there is a need to strengthen the PHO and LGU in each province on
health and climate change indicators monitoring, modeling, and analysis. The reason for
this is to enable the health sector for immediate response to address climate change
health-related problems without waiting decisions from the national level.
2. Basic weather instrumentation set up containing rain gauge, thermometer, evaporation
pan, relative humidity measurer, wind velocity meter may be funded out of the IRAs of
each of the provinces and may be installed in the municipalities. This is important to
capacitate the municipal LGUs and provincial PHOs on health and climate change
concerns so that immediate adaptation measures can be implemented right where the
problems are.
3. Intensify research on the environmental habitat of disease vectors including the climate
change conditions favoring their growth and their life cycles. Determine to what extent
does the vector live and in what conditions. Identify the types of vector, where they are
and estimate vector population so that proper strategies to control their spread may be
implemented without waiting for an outbreak. Knowing the vulnerable areas by barangay
would be a significant step in controlling diseases. In modeling, there is a need to include
environmental variables that favor the occurrences of disease vectors and their
population in addition to the climatic factors
4. Vulnerability assessment at the barangay level should be mapped for effective
implementation of adaptation measures.
5. The models are not advisable for national application due to differences in the
environmental conditions and climatic change factors in each of the provinces. Averaging
provincial data would result in substantial discrepancies between predicted values and
actual disease observations due to substantial inter-provincial variations on climatic and
environmental conditions. The only remedy is for each province to develop its own
impact models for climate change-sensitive diseases.
6. In order to address the threats of the climate change-related diseases, preventive
measures were recommended for implementation for malaria and dengue control. Thus,
the provincial governments are encouraged to fund such preventive measures to restrict
the possible spread of the diseases. The effective implementation of the preventive
measures will result in substantial savings from the income of the provincial government.
160
Other recommendations that would help the provincial governments to become more
responsive in addressing the threats of climate change-related diseases include the
following:
7. Conduct of studies on economic impacts of other climate change-related diseases such
as leptospirosis, cholera, and typhoid in Palawan and Pangasinan.
8. Conduct of studies of economic impacts of malaria, dengue, leptospirosis, cholera, and
typhoid in Rizal Province and other provinces that are vulnerable to climate change.
9. Pursue studies on the costs of other adaptation measures on health to minimize or
control the impacts of climate change-related diseases.
Recommendations from time Series Analysis
10. Regarding the generalizability of the results of the models developed, though the data
analysis had solely used data from cities of NCR, it would be potentially useful to also
apply the results to other LGUs elsewhere (i.e. other urban areas) where
communities have experienced outbreaks of dengue in the past. It is therefore
critical that local health officials should work closely with national health authorities to
coordinate efforts in mitigating the effects of a rise in temperature (i.e. recorded minimum
temperature) on a possible increase in dengue cases and/or the occurrence of dengue
outbreaks particularly during periods when an occurrence of an El Niño/La Niña –
Southern Oscillation (ENSO) condition in the Pacific Ocean is announced and
experienced.
Policy Recommendations
Policy recommendations have largely been culled from the literature as a full policy analysis
for climate change and health was not feasible within the project. The following section
contains those recommendations deemed appropriate for the Philippines.
Recommendations for stakeholders:
o A multi-stakeholder coordination committee should be established to manage
national adaptation strategies, chaired by a senior ministry.
161
o Regulatory issues should be considered from the start of the mainstreaming
process.
o The capacity of existing poverty reduction mechanisms is consistent with
existing policy criteria, development objectives and management structures.
o Policy-makers should look for policies that address current vulnerabilities and
development needs, as well as potential climate risks.
o Actions to address vulnerability to climate change should be pursued through
social development, service provision and improved natural resource
management practices.
o A broad range of stakeholders should be involved in climate change policy-
making, including civil society, sectoral departments and senior policy-
makers.
o Climate change adaptation should be informed by successful ground-level
experiences in vulnerability reduction.
o NGOs should play a dominant role in building awareness and capacity at the
local level.
Recommendations for incentives:
o Donors should provide incentives for developing country governments to take
particular adaptation actions, appropriate to local contexts.
o The economic case for different adaptation options should be communicated
widely.
o A risk-based approach to adaptation should be adopted, informed by
bottom-up experiences of vulnerability and existing responses.
o Approaches to disaster risk reduction and climate change adaptation should
be merged in a single framework, using shared tools.
162
VI. BIBLIOGRAPHY
1. Adger, WN, et al. "Assessment of adaptation practices, options, constraints and
capacity." Edited by M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and
C.E. Hanson. Climate Change 2007: Impacts, Adaptation and Vulnerability. Cambridge,
UK: Cambridge University Press, 2007. 717-743.
2. AIACC. "A stitch in time: lessons for climate change adaptation from the AIACC
project." May 2007.
http://www.aiaccproject.org/working_papers/Working%20Papers/AIACC_WP48_Leary_
etal.pdf.
3. Barclay, E. "Climate Change Spurring Dengue Rise, Experts Say." 2007.
http://news.nationalgeographic.com/news/2007/09/070921-dengue-warming.html
(accessed March 5, 2010).
4. Beaulieu, N, F Denton, V Orindi, S Carter, and S Anderson. "Using Monitoring and
Evaluation to Strengthen Climate Adaptive Capacity In Evaluating Climate Change and
Development." World Bank Series on Development, 2009: 325-335.
5. Bhattacharya, S. "Climate change and malaria in India." Current Science, 2006: 369-
375.
6. BM, Khaemba, A Mutani, and MK Bett. "Studies of anopheline mosquitoes transmitting
malaria in a newly developed highland urban area: a case study of moi university and
its environs." East African Medical Journal 3 (1994): 159-64.
7. Borja, MPB, and FME Lorenzo. "Burden of Disease and Economic Evaluation of
Dengue." 2007.
8. Bouma, MJ, and C Dye. "Cycles of malaria associated with El Niño in Venezuela."
Journal of the American Medical Association 278 (1997): 1772–1774.
9. Bouma, MJ, C Dye, and HJ Van der Kaay. "Falciparum malaria and climate change in
the northwest frontier province of Pakistan." American Journal of Tropical Medicine and
Hygiene 55 (1996): 131–137.
10. Bryan, JH, DH Foley, and RW Sutherst. "Malaria transmission and climate change in
Australia." Medical Journal ofAustralia, no. 164 (2000): 345–347.
11. Burton, I, E Diringer, and J Smith. Adaptation to climate change: international policy
options. Virginia: The Pew Center on Global Climate Change, 2006.
12. Center for Science in the Earth System at the University of Washington. Preparing for
climate change: a guidebook for local, regional and state governments. King County,
Washington: University of Washington, 2007.
13. Colwell, R. "Global climate and infectious disease: the cholera paradigm." Science,
2025-2032.
163
14. Confalonieri, U et al. Human health. In: Parry, ML et al. "Climate change. Impacts,
adaptation and vulnerability." Contribution of Working Group II to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, 2007: 391–
431.
15. Conway, D. "Extreme rainfall events and lake level changes in east africa: recent
events and historical precedents." 2002.
16. Costello, A, M Abbas, and A Allen. "Managing the health effects of climate change."
The Lancet 373 (2009): 1693–733.
17. Dankelman, I. "Climate change: learning from gender analysis and women‗s
experiences of organising for sustainable development." Gender and Development
(Taylor & Francis, Ltd) 10, no. 2 (2002): 21–9.
18. De Savigny, D, et al. "Highland malaria in Uganda: prospective analysis of an epidemic
associated with el niño." Transactions of the Royal Society of Tropical Medicine and
Hygiene 93 (2004): 480-487.
19. Denton, F. "Climate change vulnerability, impacts and adaptation: why does gender
matter?" Gender and Development 10, no. 2 (2002): 10–20.
20. Department of Health. "Philippine Health Statistics 1960 – 2005." 2011.
http://www.doh.gov.ph/health_picture.
21. Dore, MHI, and I Burton. "A Review of the literature on the costs of adaptation to
climate change." The costs of adaptation to climate change in Canada: a stratified
estimate by sectors and regions. Brock University. 2000.
http://adaptation.nrcan.gc.ca/projdb/pdf/79b_e.pdf.
22. Duque, IFT. "Interim guidelines on the prevention of leptospirosis through the use of
prophylaxis in areas affected by floods." Department Memorandum No. 2009-0250.
Vol. 10. Sta. Cruz, Manila: DOH.
23. Easterling, W E, BH Hurd, and JB Smith. Coping with global climate change: the role of
adaptation in the United States. Arlington, Virginia: The Pew Center on Global Climate
Change, 2004.
24. Ebi, K, G Meehl, and D Bachelet. Regional impacts of climate change: four case
studies in the United States. Arlington, Virginia: The Pew Center on Global Climate
Change, 2007.
25. European Commission. "Towards a comprehensive climate agreement in
Copenhagen." United Naitons Climate Change Conference. 2009. http://documents.eu-
japan.eu/seminars/japan/climate/report_climatechange_agreement.pdf.
26. Fankhauser, S] Smith, JB, and Tol RSJ. Weathering Climate Change: Some Simple
Rules to Guide Adaptation Decisions. Amsterdam: Institute for Environmental Studies,
Vrije Universiteit, 1998.
27. Gagnon-Lebrun, F. Progress on adaptation to climate change in developed countries:
an analysis of broad trends. Paris, France: OECD, 2006.
164
28. Githeko, A, and A Woodward. "Climate change and human health: risks and
responses." In International consensus on the science of climate and health: The IPCC
Third Assessment Report, edited by A, Campbell- Lendrum D, Corvalan C, Ebi K,
Githeko A K, Scheraga J S and Woodward A McMichael, 43 – 60. Geneva: WHO,
2003.
29. Githeko, AK, and W Ndegwa. "Predicting malaria epidemics in the kenyan highlands
using climate data: a tool for decision makers." Global change and Human Health 2
(2001): 54-63.
30. Global Environment Facility. "Developing socio-economic scenarios: for use in
vulnerability and adaptation assessments." In National Communications Support Unit
Handbook, by United Nations Development Programme. 2003.
31. Haines, A, D Campbell-Lendrum, and C Corvalan. "Climate change and human health:
impacts, vulnerability, and mitigation." The Lancet 367, no. 9528: 9.
32. Hales, S et al. "El Nino and the dynamics of vector-borne disease transmission."
Environmental Health Perspectives 107 (1999): 99–102.
33. Hay, SI, et al. "Defining and detecting malaria epidemics in the highlands of western
Kenya." Emerging Infectious Diseases, 2002: 555-62.
34. Intergovernmental Panel on Climate Change. "Climate Change 2007: Climate Change
Impacts, Adaptation and Vulnerability." 2007. http://www.ipcc.ch/SPM6avr07.pdf.
35. Keim, M. "Building human resilience: the role of public health preparedness and
response as an adaptation to climate change." American Journal of Preventive
Medicine 35, no. 5: 9.
36. Kilian, AHD, P Langi, A Talisuna, and G Kabagambe. "Rainfall pattern, el ñino and
malaria in Uganda." Transactions of the Royal Society of Tropical Medicine and
Hygiene 93 (2001): 22-23.
37. Kovats, RS, D Campbell-Lendrum, AJ McMichael, A Woodward, and J Cox. "Early
effects of climate change: do they include changes in vector borne diseases?"
Philosophical Transactions of the Royal Society ofLondon, 2001: 1057–1068.
38. Leary, N. "A framework for benefit-cost analysis of adaptation to climate change and
climate variability." Mitigation and Adaptation Strategies for Global Change 4 (1999):
307-318.
39. Lindblade, KA, ED Walker, AW Onapa, J Katunge, and ML Wilson. "Land use change
alters malaria transmission parameters by modifying temperatures in a highland area of
Uganda." Tropical Medicine and International Health 5, no. 4 (2000): 263-274.
40. Lindsay, SW, and MH Birley. "Climate change and malaria transmission." Annals of
Tropical Medicine and Parasitology 90 (1996): 573–588.
41. Lindsay, SW, and WJ Martens. "Malaria in the african highlands: past, present and
future." Bulletin of the World Health Organization 76 (1998): 33–45.
42. Lipp, EK, A Huq, and RR Colwell. "Effects of global climate on infectious disease: the
cholera model." Clinical Microbiology Reviews 15, no. 4 (2002): 757-770.
165
43. Loevinsohn, ME. "Climate warming and increased malaria in Rwanda." The Lancet 343
(1994): 714-748.
44. Lorenzo, FME, et al. Malaria Burden of Disease and Quality of Life Study. Institute of
Health Policy and Development Studies, National Institutes of Health, Manila,
Philippines: Department of Health, 2004.
45. Martens, P et al. "Climate change and future populations at risk of malaria." Global
Environmental Change 9 (1999): S89–S107.
46. Martens, WJM, TH Jetten, and DA Focks. "Sensitivity of malaria, schistosomiasis and
dengue to global warming." Climate Change 35 (1997): 145–156.
47. Martens, WJM, TH Jetten, J Rotmans, and LW Niessen. "Climate change and vector-
borne diseases: a global modelling perspective." Global Environmental Change 5, no. 3
(1995): 195–209.
48. Martin, PH, and MG Lefebvre. "Malaria and climate: sensitivity of malaria potential
transmission to climate." Ambio 24, no. 4 (1995): 200–207.
49. Mathers, CD, ET Vos, CE Stevenson, and SJ Begg. "The burden of disease and injury
in Australia." Bulletin of the World Health Organization 79, no. 11 (2001): 1076-84.
50. McKenzie-Hedger, M, I Brown, R Connell, and M Gawith. Climate change: assessing
the impacts – indentifying responses. London: Department of the Environment,
Transport and Regions, UK Climate Impacts Programme, 2000.
51. Mhalu, FS, WM Mntenga, and FD Mtango. "Seasonality of cholera in tanzania: possible
role of rainfall in disease transmission." East African Medical Journal 64, no. 6 (1987):
378-87.
52. Mitchell, T, T Tanner, and E Wilkinson. Overcoming the barriers: mainstreaming
climate change adaptation in developing countries. Institute of Development Studies
and Tearfund, 2006.
53. Mouchet, J, et al. "Evolution of malaria for the past 40 years: impact of climate and
human factors." Journal of American Mosquitoe Control Association 14: 121-130.
54. Murray, CJL, and AD Lopez. Global burden of disease. Vol. 1. Cambridge, UK: Harvard
School of Public Health, 1996.
55. National Statistics Office. "2007 census of population." National Statistics Office.
http://www.census.gov.ph/data/census2007/index.html (accessed 2011).
56. —. National Statistics Office. 2011. www.census.gov.ph.
57. —. "Total population for NCR for 2000 to 2006 and 2008 to 2009." National Statistics
Office. http://www.census.gov.ph/data/sectordata/popproj_tab3r.html (accessed 2011).
58. Nicholls, RJ. "Coastal flooding and wetland loss in the 21st century: changes under the
SRES climate and socio-economic scenarios." Global Environmental Change 14
(2004): 69–86.
166
59. NOAA. "Climate of 2007 - in Historical Perspective." NOAA Satellite and Information
Service. National Climatic Data Center. 2007.
http://www.ncdc.noaa.gov/oa/climate/research/2007/ann/ann07.html.
60. Nurse, L et al. "Small island states, In climate change 2001: impacts, adaptation, and
vulnerability." In Contribution of Working Group II to the Third Assessment Report of
the Intergovernmental Panel on Climate Change, by JJ et al McCarthy, 842–975.
Cambridge: Cambridge University Press, 2001.
61. Office, National Statistics. "Total Philippine population data for 2000 to 2006 and 2008
to 2009." National Statistics Office.
http://www.census.gov.ph/data/sectordata/popproj_tab1r.html (accessed 2011).
62. Oxfam International. Adapting to climate change: what„s needed in poor countries, and
who should pay. Oxfam Briefing Paper 104, UK: Oxfam International Secretariat, 2007.
63. Parry, M, and T Carter. Climate Impact and Adaptation Assessment. A Guide to the
IPCC Approach. London, UK: Earthscan Publications Ltd, 1998.
64. Pascual, M, R Xavier, SP Ellner, R Colwell, and MJ Bouma. "Cholera dynamicsand el
niño - southern oscillation." Science 289, no. 5485 (2000): 1766-1769.
65. Pascual, MMJ, AP Bouma, and Dobson AP. "Cholera and climate: revisiting the
quantitative evidence." Microbes and Infection 4 (2002): 237-245.
66. Patz, J. "A human disease indicator for the effects of recent global climate change."
PNAS 99, no. 20 (2002): 12506-12508.
67. Patz, JA. "Malaria risk and temperature: influences from global climate change and
local land use practices." Proceedings of the National Academy of Sciences of the
United States of America. 2006. 5635–5636.
68. Patz, JA, and WJM Martens. "Climate impacts on vector-borne disease transmission:
global and site-specific." Journal of Epidemiology 6 (1996): S145– S148.
69. Patz, JA, et al. "Predicting key malaria transmission factors, biting, and entomological
inoculation rates, using modeled soil moisture in Kenya." Tropical Medicine and
International Health 3 (1998): 818-827.
70. Programme, United Nations Development. "Adaptation policy frameworks for climate
changeAdaptation policy frameworks for climate change." In Developing Strategies,
Policies and Measures. Cambridge, UK: Cambridge University Press, 2004.
71. PTFCC. The Presidential Task Force on Climate Change. 2007.
http://www.doe.gov.ph/cc/.
72. R. Sari Kovats, BM, AJ McMichael, C Corvalan, and R Bertollini. Climate change and
human health: impact and adaptation. World Health Organization, 2000.
73. Reeves, WC. "Potential effect of global warming on mosquito-borne arboviruses."
Journal of Medical Entomology 31 (1994): 323–332.
74. Reiter, P. "Climate change and mosquito-borne Disease." Environmental Health
Perspectives 109 (2001): 1.
167
75. Reiter, P. "Global warming and vector-borne disease in temperate regions and at high
altitude." The Lancet 351 (1998): 839–840.
76. Rivera, PT, BA Crisostomo, EA Villacorte, AD Escueta, and et al. Climate Change and
Malaria. Edited by PT Rivera, BA Crisostomo, RU Angluben and Shigeyuki, K Kano.
Tokyo: Free Press Co, 2009.
77. —. GIS and Malaria Surveillance. Edited by PT Rivera and K Shigeyuki. Tokyo: Free
Press Co, 2007.
78. Rodo, X. "ENSO and cholera: a non-stationary link related to climate change?"
Proceedings of the National Academy of Sciences of the United States of America.
2002. 12901–12906.
79. Rosenzweig, C, A Iglesias, XB Yang, PR Epstein, and E Chivian. "Climate change and
extreme weather events: implications for food production, plant diseases and pests." In
Global change & human health, 90-10. Kluwer Academic Publishers, 2001.
80. Rueda LM, et al. "Temperature-dependent development and survival rates of culex
quinquefasciatus and aedes aegypti (diptera: culicidae)." Journal of Medical
Entomology 27 (1990): 892–898.
81. Russell, RC. "Mosquito-borne arboviruses in australia: the current scene and
implications of climate change for human health." International Journal of Parasitology
28 (1998): 955–969.
82. Schellenberg, A. J.R.M et al. "Effect of large-scale social marketing of insecticide-
treated nets on child survival in rural Tanzania." The Lancet. August 2004.
http://www.mwanzacommunity.org/mazingiranews.html.
83. Schneider, SH, et al. "Assessing key vulnerabilities and the risk from climate change."
In Climate Change 2007: Impacts, Adaptation and Vulnerability, edited by M Parry, O
Canziani, J Palutikof and PJ van der Linden. Contribution of Working Group II to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change
(forthcoming), 2007.
84. SI, Curto de Casas, and Carcavallo RU. "Climate change and vector-borne diseases
distribution." Social Science and Medicine 40 (1995): 1437–1440.
85. Stern, N. "The stern review on the economics of climate change." HM Treasury. 2006.
http://www.hm-treasury.gov.uk/independent_
reviews/stern_review_economics_climate_change/ sternreview_index.cfm.
86. Studies, Institute of Development. Adapting to climate change: Challenges and
opportunities for the developing community. Tearfund, 2006.
87. Sutherst, R. "Implications of global change and climate variability for vector-borne
diseases: generic approaches to impact assessments." International Journal for
Parasitology 28 (1998): 935–945.
88. Tanser, FC, B Sharp, and D le Sueur. "Potential effect of climate change on malaria
transmission in Africa." The Lancet 362 (2003): 1792–1798.
168
89. Tol, RSJ. "New estimates of the damage costs of climate change, part I: benchmark
estimates." Environmental and Resource Economics 21, no. 1 (2002): 47-73.
90. Tun-Lin, W, et al. "Reducing costs and operational constraints of dengue vector control
by targeting productive breeding places: a multi-country non-inferiority cluster
randomized trial." Tropical Medicine International Health 14, no. 9 (2009): 11.
91. UNFCCC. "Background paper – Impacts, vulnerability and adaptation to climate
change in Asia." UNFCCC Secretariat. http://unfccc.int/files/
adaptation/methodologies_for/vulnerability_and_
adaptation/application/pdf/unfccc_asian_workshop_background_paper.pdf.
92. —. "Background paper – Impacts, vulnerability and adaptation to climate change in
Latin America." UNFCCC Secretariat. 2006.
http://unfccc.int/files/adaptation/adverse_effects_and_response_measures_art_48/appli
cation/pdf/200609_background_latin_ american_wkshp.pdf.
93. —. "Background paper on Impacts, vulnerability and adaptation to climate change in
africa." UNFCCC Secretariat. 2006.
http://unfccc.int/files/adaptation/adverse_effects_and_response_measures_art_48/appli
cation/pdf/200609_background_african_ wkshp.pdf.
94. —. "Synthesis report on technologies for adaptation identified in the submissions from
Parties and relevant organizations." UNFCCC Secretariat. 2007. http://unfccc.int/
resource/docs/2007/sbsta/eng/06.pdf.
95. —. "Vulnerability and adaptation to climate change in small island developing states –
Background paper for the expert meeting on adaptation for small island developing
States." UNFCCC Secretariat. 2007.
http://unfccc.int/files/adaptation/adverse_effects_and_response_measures_art_48/appli
cation/pdf/ 200702_sids_adaptation_bg.pdf.
96. W, Checkley, et al. "Effects of El Nino and ambient temperature on hospital admissions
for diarrhoeal diseases in peruvian children." The Lancet 355 (2000): 442-450.
97. Watson, RT et al. "Climate change; impacts, adaptations and mitigation of climate
change: scientific-technical analysis." In Contribution of Working Group II to the Second
Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge:
Cambridge University Press, 1996.
98. Watts, DM et al. "Effect of temperature on the vector efficiency of aedes aegypti for
dengue 2 virus." American Journal of Tropical Medicine and Hygiene 36 (1987): 143–
152.
99. Wei-Chun, T, CC Chen, C Chin-Cheng, and C Yu-Hsien. "Climate Change: Estimating
the economic impacts of climate change on infectious diseases: a case study on
dengue fever in taiwan." Springer Science + Business Media B.V, 2008: 123–140.
100. WHO. "Climate change – quantifying the health impact at national and local levels."
Vers. 14. Environmental Burden of Disease Series. 2007.
http://whqlibdoc.who.int/publications/2007/9789241595674_eng.pdf.
101. —. Climate change and health. Geneva: World Health Organization, 2006.
169
102. —. Climate change and human health: impact and adaptation. Geneva: World Health
Organization, 2000.
103. —. Floods: climate change and adaptation strategies for human health. London, United
Kingdom: World Health Organization Regional Office for Europe, 2002.
104. —. "Human health impacts from climate variability and climate change in the Hindu
Kush Himalaya region : a report of an interregional workshop, India, 2005." World
Health Organization. 2006. http://www.searo.who.int/en/Section23/
Section1001/Section1110_11692.htm.
105. —. "Human leptospirosis: guidance for diagnosis, surveillance and control." 2003.
http://www.who.int/zoonoses/resources/Leptospirosis/en/.
106. —. Protecting Health from Climate Change. Geneva: World Health Organization, 2009.
107. —. "Using climate to predict infectious disease epidemics." 2005.
http://www.who.int/globalchange/publications/infectdiseases/en/index.html.
108. World Bank. World Development Report 1993: Investing in Health. Oxford University
Press: Oxford University Press, 1993.
